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Ihor K, Big Data & Data Science Engineer with BI & DevOps skills

Ukraine
Last Updated: 5 Mar 2024
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- Data Engineer with a Ph.D. degree in Measurement methods, Master of industrial automation - 16+ years experience with data-driven projects - Strong background in statistics, machine learning, AI, and predictive modeling of big data sets. - AWS Certified Data Analytics. AWS Certified Cloud Practitioner. - Experience in ETL operations and data curation - PostgreSQL, SQL, Microsoft SQL, MySQL, Snowflake - Big Data Fundamentals via PySpark, Google Cloud, AWS. - Python, Scala, C#, C++ - Skills and knowledge to design and build analytics reports, from data preparation to visualization in BI systems.

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AWS big data services

AWS big data services

AWS Quicksight

AWS Quicksight

Python

Python

Apache Kafka

Apache Kafka

Data Pipelines (ETL)

Data Pipelines (ETL)

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Nata L, Data Analyst with BI and Data Engineering skills (Senior Certified Tableau Engineer)

Germany
Last Updated: 16 Oct 2023
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- Oriented Data and Business Intelligence Analysis engineer with Data Engineering skills. - 6+ years of experience with Tableau - Experience in Operations analysis, building charts & dashboards - 20+ years of experience in data mining, data analysis, and data processing. Unifying data from many sources to create interactive, immersive dashboards and reports that provide actionable insights and drive business results. - Adept with different SDLC methodologies - Waterfall, Agile SCRUM - Knowledge of performing data analysis, data modeling, data mapping, batch data processing, and capable of generating reports using reporting tools such as Power BI (advanced), Sisence(Periscope) (expert), Tableau (Advanced), Data Studio (Advanced) - Experience in writing SQL Queries, Big Query, Python, R, DAX to extract data and perform Data Analysis - AWS, Redshift - Combined expertise in data analysis with solid technical qualifications. - Advanced English, Intermediate German - Location: Germany

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Python

Python   6 yr.

SQL

SQL   8 yr.

Microsoft Power BI

Microsoft Power BI

Data Analysis Expressions (DAX)

Data Analysis Expressions (DAX)   4 yr.

Tableau

Tableau   6 yr.

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Serg K., QA Engineer with QA Automation Engineer, Data Quality

Lviv, Ukraine
Last Updated: 4 Jul 2023
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- 5 years with QA automation - Strong testing theory understanding. Defect life cycle and issue workflow understanding, Experience in creating and executing test cases. Reporting - Experience with data and BI tools - DevOps experience with CI/CD, pipelines, Docker, AWS, SQL

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QA Automation

QA Automation   5 yr.

Python

Python

CI/CD

CI/CD

Amazon Web Services (AWS)

Amazon Web Services (AWS)

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Ankit A., Lead Data Quality Engineer

Bengaluru, India
Last Updated: 4 Jul 2023

- 5+ years of experience in the IT industry as a Software Developer with hands-on experience with technologies like Talend Studio, My SQL, Postgres, Google Data Studio, Snowflake, Matillion, and Supermetrics. - Developed Talend jobs and Matillion jobs for extracting, transforming, and loading (ETL) data in accordance with the Business logic of the Product. Analyzing, understanding, and updating the Existing Talend and Matillion jobs. - Implemented automation of SQL Queries scripts for daily, weekly & monthly reports. - Worked on Snowflake to create Stored Procedures, Streams, and Tasks. Data Validation and data quality checks at the backend with SQL. - Worked on different Cloud sources like Google Analytics, Google Ads, Facebook Ads, Google Search Console etc - Upper intermediate English - Available for buyout

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SQL

SQL

Talend

Talend

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Oleksandr T., BI Analyst

Odesa, Ukraine
Last Updated: 20 Dec 2023

- Experienced BI Analyst with a diverse background in data analysis, data engineering, and data visualization - Proficient in utilizing various BI tools such as PowerBI, Tableau, Metabase, and Periscope for creating reports and visualizations. - Skilled in exploratory data analysis using Python/pandas or SQL, as well as data manipulation in Excel - Experienced in database engineering and ETL processes using airflow/prefect/databricks as an orchestration tool and dbt for transformations. - Knowledge of data governance and implementing data standards. - DB: Postgres, BigQuery/Snowflake. - Advanced English

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Microsoft Power BI

Microsoft Power BI

Tableau

Tableau

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Henry Akwerigbe
Henry Akwerigbe
This is a super team to work with. Through Upstaff, I have had multiple projects to work on. Work culture has been awesome, teammates have been super nice and collaborative, with a very professional management. There's always a project for you if you're into tech such Front-end, Back-end, Mobile Development, Fullstack, Data Analytics, QA, Machine Learning / AI, Web3, Gaming and lots more. It gets even better because many projects even allow full remote from anywhere! Nice job to the Upstaff Team 🙌🏽.
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Владислав «Sheepbar» Баранов
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Yanina Antipova
Хочу виразити велику подяку за таку швидку роботу по підбору двох розробників. Та ще й у такий короткий термін-2 дні. Це мене здивувало, адже ми шукали вже цілий місяць. І знайдені кандидати нам не підходили Це щось неймовірне. Доречі, ці кандидати працюють у нас і зараз. Та надать приклад іншим працівникам. Гарного дня!)
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Наталья Кравцова
I discovered an exciting and well-paying project on Upstaff, and I couldn't be happier with my experience. Upstaff's platform is a gem for freelancers like me. It not only connects you with intriguing projects but also ensures fair compensation and a seamless work environment. If you're a programmer seeking quality opportunities, I highly recommend Upstaff.
Volodymyr
Volodymyr
Leaving a review to express how delighted I am to have found such a great side gig here. The project is intriguing, and I'm really enjoying the team dynamics. I'm also quite satisfied with the compensation aspect. It's crucial to feel valued for the work you put in. Overall, I'm grateful for the opportunity to contribute to this project and share my expertise. I'm thrilled to give a shoutout and recommendation to anyone seeking an engaging and rewarding work opportunity.

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FAQs about Snowflake Development

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If you urgently need a verified and qualified Snowflake developer, and resources for finding the right candidate are lacking, UPSTAFF is exactly the service you need. We approach the selection of Snowflake developers professionally, tailored precisely to your needs. From placing the call to the completion of your task by a qualified developer, only a few days will pass.

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Undoubtedly, there are dozens, if not hundreds, of specialized services and platforms on the network for finding the right Snowflake engineer. However, only UPSTAFF offers you the service of selecting real qualified professionals almost in real time. With Upstaff, software development is easier than calling a taxi.

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AI tools and expert human reviewers in the vetting process are combined with a track record and historically collected feedback from clients and teammates. On average, we save over 50 hours for client teams in interviewing Snowflake candidates for each job position. We are fueled by a passion for technical expertise, drawn from our deep understanding of the industry.

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Our journey starts with a 30-minute discovery call to explore your project challenges, technical needs, and team diversity. Meet Carefully Matched Snowflake Talents. Within 1-3 days, we’ll share profiles and connect you with the right talents for your project. Schedule a call to meet engineers in person. Validate Your Choice. Bring a new Snowflake developer on board with a trial period to confirm that you’ve hired the right one. There are no termination fees or hidden costs.

How does Upstaff vet remote Snowflake engineers? Arrow

Upstaff Managers conduct an introductory round with potential candidates to assess their soft skills. Additionally, the talent’s hard skills are evaluated through testing or verification by a qualified developer during a technical interview. The Upstaff Staffing Platform stores data on past and present Snowflake candidates. Upstaff managers also assess talent and facilitate rapid work and scalability, offering clients valuable insights into their talent pipeline. Additionally, we have a matching system within the platform that operates in real-time, facilitating efficient pairing of candidates with suitable positions.

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Table of Contents

What are top Snowflake instruments and tools?

  • Snowflake Data Exchange: Snowflake Data Exchange is a marketplace where Snowflake customers can discover, access, and share data sets. It enables organizations to easily find and integrate third-party data sources into their Snowflake account, allowing for data enrichment and analysis.
  • Snowflake Data Marketplace: The Snowflake Data Marketplace is a platform that offers a wide range of data sets from various data providers. It provides a central location for users to find and access data sets that can be seamlessly integrated into their Snowflake environment. It includes data sets from categories such as finance, healthcare, retail, and more.
  • SnowSQL: SnowSQL is a command-line tool provided by Snowflake that allows users to interact with their Snowflake account using SQL commands. It provides a convenient way to query and manage data in Snowflake without the need for a graphical user interface.
  • Snowflake Secure Data Sharing: Snowflake Secure Data Sharing is a feature that allows organizations to securely share live, governed data with external parties. It enables seamless collaboration between different organizations, making it easier to share and analyze data across multiple entities.
  • Snowflake Partner Connect: Snowflake Partner Connect is a feature that enables users to easily discover and connect with Snowflake’s extensive network of technology and consulting partners. It provides a streamlined process for finding and engaging with partners who can help with various aspects of Snowflake implementation and usage.
  • Snowflake Data Cloud: The Snowflake Data Cloud is a global network of cloud-based data centers that host Snowflake’s infrastructure. It ensures high availability, scalability, and performance for Snowflake customers, allowing them to seamlessly access and analyze their data from anywhere in the world.
  • Snowflake Workbook: Snowflake Workbook is a collaborative, web-based environment within Snowflake where users can create, share, and execute SQL queries and visualizations. It provides a user-friendly interface for data exploration and analysis, making it easier for users to derive insights from their data.
  • Snowflake Account Usage Dashboard: The Snowflake Account Usage Dashboard is a monitoring and reporting tool that provides visibility into the usage and performance of a Snowflake account. It enables users to track and analyze their resource utilization, query performance, and other key metrics to optimize their Snowflake environment.
  • Snowflake Data Pipelines: Snowflake Data Pipelines is a feature that allows users to build automated data integration workflows within Snowflake. It provides a visual interface for designing and orchestrating data pipelines, making it easier to ingest, transform, and load data into Snowflake from various sources.
  • Snowflake Snowsight: Snowflake Snowsight is a modern, browser-based analytics interface within Snowflake that enables users to visualize and explore their data. It includes a range of interactive visualizations and collaboration features, empowering users to gain insights and share findings with others.

Soft skills of a Snowflake Developer

Soft skills are essential for a successful career as a Snowflake Developer. These skills, in addition to technical expertise, enable developers to effectively communicate, collaborate, and problem-solve. Here are the soft skills required at different levels of experience:

Junior

  • Adaptability: Demonstrates the ability to quickly learn and adapt to new technologies and methodologies.
  • Communication: Effectively communicates ideas and technical concepts to both technical and non-technical stakeholders.
  • Teamwork: Works collaboratively with team members to achieve project goals and meet deadlines.
  • Problem-solving: Uses critical thinking skills to analyze and resolve technical issues.
  • Attention to Detail: Pays close attention to detail to ensure accuracy and quality in data handling and analysis.

Middle

  • Leadership: Takes on leadership responsibilities within the development team, guiding and mentoring junior developers.
  • Time Management: Efficiently manages time and prioritizes tasks to meet project deadlines.
  • Conflict Resolution: Resolves conflicts within the team by actively listening, mediating, and finding mutually beneficial solutions.
  • Client Management: Builds strong relationships with clients, understanding their needs and providing appropriate solutions.
  • Continuous Learning: Actively engages in continuous learning to stay updated with the latest advancements in Snowflake and related technologies.
  • Creativity: Applies creative thinking to develop innovative solutions for complex business problems.
  • Adaptability: Quickly adapts to changing project requirements and client needs.

Senior

  • Strategic Thinking: Demonstrates a strategic mindset, aligning technical solutions with business objectives.
  • Project Management: Manages and leads projects, ensuring successful delivery within scope, budget, and timeline.
  • Decision Making: Makes informed decisions based on analysis, experience, and understanding of the business context.
  • Empathy: Understands and empathizes with the needs and perspectives of team members, clients, and stakeholders.
  • Influence: Influences and persuades stakeholders to adopt best practices and make informed decisions.
  • Collaboration: Collaborates effectively with cross-functional teams to achieve shared goals.
  • Strategic Communication: Communicates complex technical concepts to non-technical stakeholders, adapting the message to the audience.
  • Knowledge Sharing: Shares knowledge and expertise with the team, fostering a culture of continuous learning.

Expert/Team Lead

  • Strategic Leadership: Provides strategic direction to the team, aligning technology initiatives with organizational goals.
  • Team Management: Builds and manages high-performing teams, nurturing talent and fostering a positive work environment.
  • Innovation: Drives innovation within the organization, exploring new technologies and approaches to enhance efficiency and effectiveness.
  • Client Engagement: Builds strong relationships with clients, understanding their business needs and providing strategic guidance and solutions.
  • Conflict Resolution: Resolves complex conflicts and challenges, leveraging advanced negotiation and mediation skills.
  • Thought Leadership: Demonstrates thought leadership in the Snowflake ecosystem through contributions to industry forums, conferences, and publications.
  • Business Acumen: Possesses a deep understanding of the business landscape, leveraging technology to drive business value.
  • Strategic Partnerships: Establishes and maintains strategic partnerships with Snowflake and other technology vendors.
  • Risk Management: Identifies and mitigates risks associated with technology implementations and operations.
  • Executive Presence: Presents confidently and effectively to executive stakeholders, conveying technical concepts in a clear and concise manner.
  • Change Management: Leads organizational change initiatives, driving adoption of new technologies and processes.

TOP 10 Snowflake Related Technologies

  • Python

    Python is a versatile and widely-used programming language for snowflake software development. It offers an extensive range of libraries and frameworks that facilitate rapid development and efficient coding. Python’s simplicity and readability make it an ideal choice for beginners and experienced developers alike.

  • JavaScript

    JavaScript is another popular language used in snowflake software development. It is primarily used for web-based applications and provides excellent support for client-side scripting. JavaScript frameworks like React and AngularJS are widely used for building interactive and responsive user interfaces.

  • Java

    Java is a robust and platform-independent language widely used for enterprise-level snowflake software development. It offers a vast ecosystem of libraries and frameworks that enable developers to build scalable and secure applications. Java’s strong typing and strict syntax ensure code reliability and maintainability.

  • HTML/CSS

    HTML and CSS are essential technologies for web development. HTML provides the structure and content of a web page, while CSS is responsible for the presentation and styling. Snowflake software often involves web-based interfaces, making HTML/CSS skills crucial for front-end development.

  • SQL

    Structured Query Language (SQL) is a standard language for managing and manipulating databases. Snowflake software development often involves working with databases, and SQL proficiency is vital for querying and managing data efficiently. Understanding complex SQL queries and optimization techniques is highly beneficial.

  • AWS

    Amazon Web Services (AWS) is a cloud computing platform that offers a wide range of services for snowflake software development. With AWS, developers can easily deploy, scale, and manage applications. Familiarity with AWS services like EC2, S3, and Lambda can greatly enhance development capabilities.

  • Docker

    Docker is a popular containerization platform that allows developers to package applications and their dependencies into isolated containers. It simplifies the deployment process and ensures consistent behavior across different environments. Knowledge of Docker is essential for building scalable and portable snowflake software.

Pros & cons of Snowflake

9 Pros of Snowflake

  • 1. Scalability: Snowflake offers near-infinite scalability, allowing organizations to easily handle large amounts of data without worrying about infrastructure limitations.
  • 2. Performance: Snowflake’s unique architecture enables fast query performance, thanks to its separation of compute and storage layers, as well as its automatic query optimization capabilities.
  • 3. Elasticity: With Snowflake, users can dynamically provision and scale compute resources as needed, ensuring optimal performance during peak workloads and cost-efficiency during low-activity periods.
  • 4. Data Sharing: Snowflake allows for secure and seamless data sharing between different organizations, departments, or teams, enabling collaboration and reducing data duplication.
  • 5. Security: Snowflake has built-in security features such as encryption, access controls, and data masking, ensuring the confidentiality and integrity of data stored in the platform.
  • 6. Zero-copy cloning: Snowflake’s zero-copy cloning feature allows for rapid and cost-effective creation of copies of data, without duplicating the underlying data, leading to significant time and cost savings.
  • 7. Automatic backups and disaster recovery: Snowflake automatically takes care of backups and provides robust disaster recovery capabilities, minimizing the risk of data loss and ensuring business continuity.
  • 8. Data integration: Snowflake supports seamless integration with various data sources and data integration tools, making it easier to bring in data from multiple systems and perform comprehensive analysis.
  • 9. Cost-effective: Snowflake’s pay-as-you-go pricing model, combined with its ability to optimize resource utilization, allows organizations to control costs and pay only for the resources they actually use.

9 Cons of Snowflake

  • 1. Learning curve: While Snowflake offers a user-friendly interface, there is still a learning curve involved in understanding and effectively utilizing its various features and capabilities.
  • 2. Dependency on internet connectivity: As a cloud-based platform, Snowflake requires a reliable internet connection for users to access and work with their data.
  • 3. Cost considerations: While Snowflake’s pricing model can be cost-effective, organizations with specific usage patterns or high data volumes may need to carefully plan and monitor their usage to avoid unexpected costs.
  • 4. Limited control over infrastructure: Snowflake’s managed service model means that users have limited control over the underlying infrastructure, which may be a concern for organizations with specific security or compliance requirements.
  • 5. Dependency on cloud providers: Snowflake relies on cloud providers such as Amazon Web Services (AWS) or Microsoft Azure, and any disruptions or issues with these providers can impact the availability and performance of the Snowflake platform.
  • 6. Limited offline access: As a cloud-based platform, Snowflake requires an internet connection for users to access and work with their data, limiting offline access capabilities.
  • 7. Data egress costs: While data ingress into Snowflake is typically free, there may be costs associated with data egress, i.e., transferring data out of Snowflake, which organizations should consider when planning their data workflows.
  • 8. Limited support for unstructured data: While Snowflake excels at handling structured and semi-structured data, it may have limitations when it comes to working with complex unstructured data types.
  • 9. Vendor lock-in: Due to its proprietary nature, transitioning away from Snowflake to another data warehousing solution can be challenging, potentially leading to vendor lock-in.

How and where is Snowflake used?

Case NameCase Description
Data WarehousingSnowflake’s architecture allows for scalable and elastic data warehousing, making it an ideal platform for organizations to store and analyze large volumes of data. With its ability to handle structured and semi-structured data, Snowflake enables businesses to perform complex analytics and gain valuable insights from their data.
Data Lake AnalyticsSnowflake’s integration with data lakes enables organizations to query and analyze data directly from their data lake storage. This eliminates the need to move data between systems, providing a seamless and efficient data analytics experience. By leveraging Snowflake’s powerful computing capabilities, businesses can derive meaningful insights from their data lake without the complexity of managing multiple systems.
Real-Time AnalyticsSnowflake’s near-zero maintenance architecture and automatic scaling capabilities make it an excellent choice for real-time analytics. Organizations can ingest and analyze streaming data in real-time, enabling them to make data-driven decisions and respond quickly to changing business conditions. Snowflake’s ability to handle high concurrency allows multiple users to execute complex queries simultaneously, ensuring efficient real-time analytics for businesses.
Data SharingSnowflake’s data sharing feature allows organizations to securely share data with external partners, customers, or other business units. By creating secure data sharing links, businesses can collaborate and exchange data without the need for complex data transfers or duplicating data. This feature is particularly useful in scenarios where organizations need to share data with third parties for joint analytics, reporting, or business insights.
Advanced AnalyticsSnowflake’s integration with popular analytics and data science tools, such as Python, R, and Spark, enables organizations to perform advanced analytics and machine learning on their data. By leveraging Snowflake’s scalable computing power and seamless integration with these tools, businesses can build sophisticated models, perform predictive analytics, and uncover hidden patterns and insights in their data.
Data ReplicationSnowflake’s global data replication capabilities allow organizations to replicate data across multiple regions for disaster recovery, data sovereignty, or high availability purposes. This ensures that businesses have access to their data even in the event of a regional outage or failure. Snowflake’s automatic failover and built-in data replication features provide organizations with peace of mind and ensure continuous access to critical data.
Data GovernanceSnowflake’s comprehensive security and governance features enable organizations to enforce data privacy, compliance, and access controls. With features such as role-based access control, data masking, and auditing, businesses can ensure that sensitive data is protected and accessed only by authorized users. Snowflake’s built-in compliance certifications, including SOC 2 Type II and HIPAA, further strengthen the data governance capabilities of the platform.
Cloud Data IntegrationSnowflake’s cloud-native architecture and seamless integration with popular cloud services, such as AWS, Azure, and Google Cloud, make it an ideal choice for cloud data integration. Organizations can easily ingest, transform, and load data from various sources into Snowflake, enabling a unified view of their data across different systems. This simplifies the data integration process and accelerates time-to-insights for businesses.

TOP 11 Facts about Snowflake

  • Snowflake is a cloud-based data warehousing platform that was first introduced in 2014.
  • It was founded by three data warehousing experts: Benoit Dageville, Thierry Cruanes, and Marcin Zukowski.
  • Snowflake is built on a unique architecture called the Multi-Cluster Shared Data Architecture (MCSDA), which enables separate compute and storage layers for maximum scalability and performance.
  • The platform is designed to handle large volumes of data and support a wide range of workloads, including data analytics, business intelligence, and data engineering.
  • Snowflake’s architecture allows users to scale their compute resources up or down independently of their storage, providing flexibility and cost optimization.
  • One of the key advantages of Snowflake is its ability to support both structured and semi-structured data, making it well-suited for handling diverse data types.
  • The platform uses a technique called automatic data organization, which eliminates the need for manual data partitioning and indexing, simplifying data management tasks.
  • Snowflake’s query optimizer is built to automatically optimize and parallelize queries, ensuring fast and efficient processing of data.
  • The platform provides strong security features, including end-to-end encryption, role-based access control, and data masking, to protect sensitive data.
  • Snowflake offers a pay-as-you-go pricing model, allowing users to pay only for the resources they consume, without any upfront costs or long-term commitments.
  • Many leading organizations across various industries, including media, technology, healthcare, and finance, rely on Snowflake for their data analytics and data warehousing needs.

Cases when Snowflake does not work

  1. Limited internet connectivity: Snowflake is a cloud data warehouse, which means it relies on a stable and high-speed internet connection to function properly. In cases where there is limited or unreliable internet connectivity, Snowflake may not work as expected. This can result in slow query performance, data transfer issues, or even complete loss of connection to the Snowflake service.
  2. Insufficient compute resources: Snowflake’s performance is directly linked to the amount of compute resources allocated to it. If the compute resources provided to a Snowflake instance are insufficient for the workload demands, it may lead to slow query execution, resource contention, and degraded performance. It is essential to appropriately size the compute resources to ensure optimal performance.
  3. Unsupported data types or data structures: While Snowflake offers extensive support for various data types and structures, there might be instances where certain unsupported or incompatible data types are encountered. In such cases, Snowflake may not be able to process or store the data correctly, leading to errors or unexpected behavior.
  4. Complex real-time data processing: Snowflake is primarily designed for analytical workloads and may not be the best choice for complex real-time data processing scenarios with extremely low latency requirements. Although Snowflake provides good performance for most analytical use cases, specialized real-time processing systems might be more suitable for certain high-speed, low-latency applications.
  5. High costs for small-scale workloads: Snowflake’s pricing model is based on usage, and while it offers great scalability and performance for large-scale workloads, it may not be cost-effective for small-scale or sporadic usage. If the volume of data and the frequency of queries are relatively low, alternative solutions might be more cost-efficient.

Let’s consider Difference between Junior, Middle, Senior, Expert/Team Lead developer roles.

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior0-2 yearsAssisting with basic programming tasks, bug fixing, and code reviews under supervision. Participating in team meetings and providing input on project requirements. Learning new technologies and improving coding skills.$50,000-$70,000
Middle2-5 yearsDeveloping and implementing software solutions. Collaborating with team members to design and optimize code. Taking ownership of specific modules or components within a project. Mentoring junior developers and assisting with code reviews. Participating in technical discussions and providing input on architectural decisions.$70,000-$90,000
Senior5-8 yearsLeading the development of complex software systems. Designing and implementing scalable and efficient solutions. Mentoring and providing guidance to junior and middle developers. Collaborating with cross-functional teams to define project requirements and timelines. Participating in code reviews and ensuring adherence to coding standards. Identifying and resolving technical challenges.$90,000-$120,000
Expert/Team Lead8+ yearsLeading a team of developers and overseeing project execution. Setting technical direction and making architectural decisions. Mentoring and coaching team members to enhance their skills and performance. Collaborating with stakeholders to define project scope and objectives. Conducting code reviews and ensuring high code quality. Providing technical guidance and resolving complex technical issues.$120,000-$150,000+

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