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

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



Apache Kafka

Apache Kafka

Data Pipelines (ETL)

Data Pipelines (ETL)

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Henry A., Software Engineer with Python and Data Analytical Skills

Last Updated: 14 Mar 2024
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- 8+ years experience working with Python; - 5 years of experience as a BI and 4 years of experience with Tableau; - 8 years of experience with various data sets (ETL, Data Engineer, Data Quality Engineer); - 3 years of experience with Amazon Web Services (AWS), Google Cloud Platform (GCP); - Data Analytics/Engineering with Cloud Service Providers (AWS, GCP) - Experience working with MySQL, SQL, and PostgreSQL; - Deep abilities working with Kubernetes (K8s); - Hands-on scripting experience with Python; Microsoft Power BI, Tableau, Sisense, CI/CD principles, Data Validation, Data QA, SQL, Pipelines, ETL, and Automated web scraping. - Pet web3 projects (solidity, wallet integration) - Upper-intermediate English

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Python   8.5 yr.

Data Analysis

Data Analysis   6 yr.

Google Cloud Platform (GCP)

Google Cloud Platform (GCP)   4 yr.


Tableau   4 yr.

Microsoft Power BI

Microsoft Power BI   4 yr.

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Asad S., AWS Data Engineer

Last Updated: 4 Jul 2023

- More than 8 years of Data Engineering experience in the Banking and Health sector. - Worked on Datawarehousing and ETL pipeline projects using AWS Glue, Databrew, Lambda, Fivetran, Kinesis, Snowflake, Redshift, and Quicksight. - Recent project involves loading data into Snowflake using Fivetran connector and automation of pipeline using Lambda and Eventbridge. - Performed Cloud Data Migrations and automation of ETL pipeline design and implementations. - Fluent English - Available from 18.08.2022

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Amazon Web Services (AWS)

Amazon Web Services (AWS)

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Oleksandr V, Python Developer/Data Engineer

Last Updated: 4 Jul 2023

- 15+ years experience as Python Developer/Data Engineer. - Has experience with SQL and data modeling skills. Also, with migration from one data warehousing solution to another. Solid experience designing and implementing a complex data warehouse or database schema. And has implemented an ETL pipeline using dbt, and AWS. - Upper-Intermediate English. - Start ASAP

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SQL   15 yr.


Python   7 yr.

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Marcio A., Data Engineer

Last Updated: 4 Jul 2023

- 20+ years of experience in IT - Experience in Support Analyst, Systems Analysis, Software Development, Data Engineer, Database Engineer, Systems Integration, Integration Developer, Data Analysis, Data Structures, Data Integration, Database Migration, Database Modeler, Database Analyst/Developer, Database Design, Database Management, Data Warehouse, Data Discovery, ELT/ETL, Extract, Transform, Load, Data Pipelines, Data Replication, Business Intelligence, Data Modeling (Dimension and Relational), Performance Tuning (SQL, PL/SQL Applications), experience in working with large databases and datasets, testing, validation, implementation, and troubleshooting skills, performance and resource optimization improvements, maintaining development, test and production environments, experience with process automation (monitoring, alerting, auto-corrective) and distributed systems, strong knowledge in PL/SQL, SQL, Oracle Database and - Knowledge in Agile methodology (Scrum, Kanban). - Upper-Intermediate English - Available ASAP - No vacation plans

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Data Engineering

Data Engineering

Apache Kafka

Apache Kafka   1 yr.

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FAQs about Data Pipelines (ETL) Development

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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 Data Pipelines (ETL) 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 Data Pipelines (ETL) developer on board with a trial period to confirm that you’ve hired the right one. There are no termination fees or hidden costs.

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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 Data Pipelines (ETL) 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

Pros & cons of Data Pipelines (ETL)

9 Pros of Data Pipelines (ETL)

  • Efficient Data Integration: Data pipelines (ETL) enable efficient integration of data from multiple sources into a centralized location. This allows for easy access and analysis of data, leading to better decision-making.
  • Data Quality Improvement: ETL processes often include data cleansing and transformation steps, which help improve the quality and consistency of the data being processed. This ensures that the data used for analysis and reporting is accurate and reliable.
  • Automation and Scalability: Data pipelines can be automated to run on a schedule or triggered by specific events, reducing the need for manual intervention. Additionally, they can easily scale to handle large volumes of data, ensuring efficient processing even as data volumes grow.
  • Real-Time Data Processing: With the right tools and technologies, data pipelines can be designed to process data in near real-time. This enables organizations to make faster decisions based on up-to-date information.
  • Data Transformation and Enrichment: ETL processes allow for data transformation and enrichment, such as aggregating data, applying business rules, or combining data from different sources. This enhances the value of the data and makes it more useful for analysis.
  • Data Governance and Compliance: Data pipelines can incorporate data governance and compliance measures, ensuring that data is handled in a secure and compliant manner. This is particularly important for organizations operating in regulated industries.
  • Improved Data Accessibility: By centralizing data through ETL processes, data pipelines make it easier for users to access and analyze data. This promotes self-service analytics and empowers users to derive insights without relying on IT teams.
  • Reduced Data Latency: ETL processes can help reduce data latency, ensuring that the most up-to-date data is available for analysis. This is crucial in time-sensitive applications where real-time or near real-time insights are required.
  • Support for Data Warehousing and Business Intelligence: Data pipelines play a crucial role in supporting data warehousing and business intelligence initiatives. They enable the extraction, transformation, and loading of data into data warehouses, facilitating analytics and reporting.

9 Cons of Data Pipelines (ETL)

  • Complexity and Maintenance: Designing and maintaining data pipelines can be complex, requiring specialized knowledge and expertise. Changes in data sources or data structures may require updates to the pipeline, increasing maintenance efforts.
  • Data Loss or Inconsistency: If not implemented properly, data pipelines can lead to data loss or inconsistencies. Errors during data extraction, transformation, or loading can result in incomplete or incorrect data, impacting the accuracy of analysis.
  • Processing Overhead: ETL processes can introduce processing overhead, especially when dealing with large volumes of data. This can impact overall system performance and increase resource requirements.
  • Dependency on Source Systems: Data pipelines rely on the availability and stability of source systems. Any issues in the source systems can affect the pipeline’s ability to extract data, leading to delays or failures in data processing.
  • Data Security Risks: Data pipelines involve the movement and transformation of data, which introduces security risks. Sensitive data may be exposed during the ETL process, requiring robust security measures to protect against unauthorized access.
  • Data Timeliness: Traditional batch-based ETL processes may introduce delays in data availability, which can be a limitation in scenarios where real-time or near real-time data is required for analysis.
  • Initial Setup and Configuration: Setting up data pipelines requires initial configuration and integration with various systems and tools. This setup process can be time-consuming and may require coordination across different teams.
  • Resource Intensive: ETL processes can be resource-intensive, especially when dealing with large volumes of data or complex transformations. This may require organizations to invest in robust infrastructure to ensure efficient processing.
  • Limited Flexibility: Once a data pipeline is established, making changes to the pipeline structure or adding new data sources may require significant effort and coordination, limiting flexibility and agility.

How and where is Data Pipelines (ETL) used?

Case NameCase Description
Real-time AnalyticsData pipelines enable the ingestion of large volumes of data from various sources in real-time. This allows organizations to perform real-time analytics, providing valuable insights and enabling timely decision-making. For example, a financial institution can use data pipelines to process real-time market data and perform complex calculations to make informed investment decisions.
Data WarehousingData pipelines play a crucial role in data warehousing by extracting data from multiple sources, transforming it into a unified format, and loading it into a data warehouse. This enables organizations to consolidate and analyze data from various systems, facilitating better reporting, business intelligence, and data-driven decision-making.
Customer SegmentationData pipelines can be used to collect and process customer data from different channels, such as websites, mobile apps, and social media platforms. By integrating this data and applying segmentation algorithms, businesses can gain insights into customer behavior, preferences, and demographics, allowing for targeted marketing campaigns and personalized customer experiences.
Internet of Things (IoT) Data ProcessingData pipelines are essential in handling the massive amounts of data generated by IoT devices. They enable the collection, transformation, and analysis of IoT data, enabling organizations to monitor and optimize processes, detect anomalies, and create predictive maintenance strategies. For example, a manufacturing plant can use data pipelines to process sensor data from equipment to prevent downtime and improve operational efficiency.
Log AnalysisData pipelines are commonly used in log analysis to process and analyze large volumes of log data generated by systems, applications, and network devices. By extracting relevant information from logs and applying analytics, organizations can identify patterns, troubleshoot issues, and improve system performance. For instance, an e-commerce company can use data pipelines to analyze web server logs to detect and mitigate potential security threats.
Fraud DetectionData pipelines are instrumental in fraud detection by processing and analyzing vast amounts of data in real-time. By integrating data from multiple sources, such as transaction logs, user profiles, and historical patterns, organizations can detect and prevent fraudulent activities promptly. Financial institutions often use data pipelines to identify suspicious transactions, protecting both themselves and their customers.
Recommendation SystemsData pipelines are used in recommendation systems to gather and process user data, such as browsing history, purchase behavior, and preferences. By employing machine learning algorithms, organizations can generate personalized recommendations, enhancing the user experience and driving sales. For example, streaming platforms use data pipelines to analyze user interactions and suggest relevant content.
Supply Chain OptimizationData pipelines are utilized in supply chain optimization to collect and analyze data from various stages of the supply chain, including procurement, manufacturing, logistics, and demand forecasting. By integrating and analyzing this data, organizations can identify inefficiencies, optimize inventory levels, streamline operations, and improve overall supply chain performance.
Sentiment AnalysisData pipelines are employed in sentiment analysis to process and analyze large volumes of textual data, such as customer reviews, social media posts, and customer support interactions. By applying natural language processing techniques, organizations can extract sentiments and opinions, enabling them to understand customer feedback, track brand reputation, and make data-driven decisions to improve products and services.

Cases when Data Pipelines (ETL) does not work

  1. Insufficient Data Quality: Data pipelines rely on high-quality data to perform accurate transformations and analysis. If the incoming data is incomplete, inconsistent, or contains errors, it can lead to faulty results and disrupt the pipeline’s functionality. Poor data quality can stem from various sources, such as data entry mistakes, system glitches, or outdated data sources.
  2. Incompatible Data Formats: Data pipelines often need to handle data from diverse sources, such as databases, APIs, files, and streaming platforms. Incompatibility in data formats can pose a challenge, as different systems may use different file formats, encoding schemes, or data structures. If the pipeline is not designed to handle these variations, it can result in data parsing errors and hinder the data extraction and transformation processes.
  3. Changes in Data Sources: Data pipelines are designed based on the assumption that the structure and behavior of the data sources remain constant. However, when the underlying data sources undergo significant changes, such as schema modifications, API updates, or database migrations, the pipeline may no longer be able to fetch or process the data correctly. These changes can introduce compatibility issues and require adjustments to the pipeline configurations.
  4. Insufficient Scalability: As data volumes grow, the pipeline must be capable of handling increasing workloads efficiently. If the pipeline architecture or infrastructure is not designed to scale horizontally or vertically, it may become overwhelmed by the data load, leading to performance degradation, bottlenecks, and potential data loss. Scalability should be a key consideration when designing a data pipeline.
  5. Connectivity and Network Issues: Data pipelines often rely on network connectivity to fetch data from external sources or transmit processed data to downstream systems. Any disruptions in network connectivity, such as intermittent outages, high latency, or limited bandwidth, can impede the pipeline’s ability to fetch or transmit data. It is crucial to establish robust network infrastructure and implement error handling mechanisms to handle such connectivity issues.
  6. Security and Compliance Concerns: Data pipelines often deal with sensitive and confidential data, requiring adherence to security and compliance standards. If the pipeline lacks proper encryption, access controls, or auditing mechanisms, it can expose the data to unauthorized access, breaches, or non-compliance with regulations. Ensuring data security and compliance should be a fundamental aspect of any data pipeline implementation.
  7. Limited Monitoring and Error Handling: Without comprehensive monitoring and error handling mechanisms in place, it becomes challenging to identify and resolve issues in the data pipeline. Lack of visibility into the pipeline’s performance, data flow, or error logs can lead to undetected failures, prolonged downtime, and data inconsistencies. Implementing robust monitoring and error handling practices is essential to maintain the reliability and effectiveness of the pipeline.

TOP 12 Facts about Data Pipelines (ETL)

  • Data pipelines, also known as Extract, Transform, Load (ETL) processes, are essential for organizations to ingest, process, and analyze large volumes of data efficiently.
  • Data pipelines help ensure data integrity and consistency by transforming and cleaning data from various sources before loading it into a centralized data storage or data warehouse.
  • ETL processes typically involve extracting data from multiple sources such as databases, files, APIs, or streaming platforms.
  • The extracted data is then transformed to meet specific business requirements, including data cleaning, normalization, aggregation, and enrichment.
  • Data pipelines play a crucial role in enabling data integration, allowing organizations to combine and consolidate data from different systems or departments.
  • High-quality data pipelines help improve data accuracy, reduce errors, and enhance decision-making processes within an organization.
  • ETL processes are often automated to ensure efficiency, scalability, and repeatability, minimizing manual effort and human errors.
  • Data pipelines enable real-time or near real-time data processing, allowing organizations to make timely decisions based on the most up-to-date information.
  • Robust data pipelines can handle large data volumes and efficiently process data in parallel, ensuring optimal performance and scalability.
  • Monitoring and logging mechanisms are crucial components of data pipelines to track data flow, identify issues, and ensure data quality throughout the process.
  • Data pipelines can leverage various technologies and tools, such as Apache Kafka, Apache Spark, Apache Airflow, or cloud-based services like AWS Glue or Google Cloud Dataflow.
  • Data pipelines are essential in enabling advanced analytics, machine learning, and artificial intelligence applications, as they provide a reliable and consistent flow of data for training and prediction purposes.

What are top Data Pipelines (ETL) instruments and tools?

  • Airflow: Airflow is an open-source platform used for orchestrating and scheduling complex data pipelines. It was developed by Airbnb in 2014 and later open-sourced. Airflow allows users to define, schedule, and monitor workflows as directed acyclic graphs (DAGs). It has gained significant popularity due to its scalability, extensibility, and active community support.
  • Apache Kafka: Apache Kafka is a distributed streaming platform that is widely used for building real-time data pipelines and streaming applications. It was initially developed by LinkedIn and later open-sourced in 2011. Kafka provides high-throughput, fault-tolerant, and scalable messaging capabilities, making it suitable for handling large volumes of data in real-time.
  • Informatica PowerCenter: Informatica PowerCenter is a widely used enterprise data integration platform. It offers a comprehensive set of tools and capabilities for designing, executing, and monitoring data integration workflows. PowerCenter has been in the market for several years and is known for its robustness, scalability, and broad range of connectors and transformations.
  • Microsoft SQL Server Integration Services (SSIS): SSIS is a powerful data integration and ETL tool provided by Microsoft as part of its SQL Server suite. It offers a visual development environment for building data integration workflows and supports a wide range of data sources and destinations. SSIS has been widely adopted in the Microsoft ecosystem and is known for its ease of use and integration with other SQL Server components.
  • Talend Data Integration: Talend Data Integration is an open-source data integration platform that provides a visual development environment for designing and executing data integration workflows. It offers a wide range of connectors, transformations, and data quality features. Talend has gained popularity due to its user-friendly interface, extensive community support, and rich set of features.
  • Google Cloud Dataflow: Google Cloud Dataflow is a fully managed service for building data pipelines and processing large-scale data sets in real-time or batch mode. It offers a unified programming model based on Apache Beam, allowing developers to write data processing logic in multiple programming languages. Dataflow is known for its scalability, fault-tolerance, and integration with other Google Cloud services.
  • Amazon Glue: Amazon Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services (AWS). It offers a serverless environment for building and running data pipelines, along with a visual interface for designing data transformation workflows. Glue supports various data sources and provides features like data cataloging, data cleaning, and job scheduling.

TOP 10 Data Pipelines (ETL) Related Technologies

  • Python

    Python is a widely used programming language for data pipelines and ETL (Extract, Transform, Load) tasks. It offers a rich ecosystem of libraries and frameworks such as Pandas and NumPy, which enable efficient data manipulation and analysis. Python’s simplicity and readability make it a popular choice among data engineers and scientists.

  • Apache Spark

    Apache Spark is a powerful open-source framework for distributed data processing. It provides high-level APIs in Java, Scala, and Python, making it accessible to developers with different language preferences. Spark’s ability to handle large-scale data processing and its built-in support for ETL operations make it a valuable tool for data pipeline development.

  • Airflow

    Apache Airflow is an open-source platform for orchestrating complex data workflows. It allows developers to define and schedule data pipelines as directed acyclic graphs (DAGs), making it easier to manage dependencies and monitor pipeline execution. Airflow’s extensibility and scalability make it a popular choice for building robust and scalable data pipelines.

  • Kafka

    Apache Kafka is a distributed streaming platform that can be used for building real-time data pipelines. It provides high-throughput, fault-tolerant messaging capabilities, allowing data to be ingested and processed in real-time. Kafka’s scalability and durability make it a popular choice for streaming data integration and ETL workflows.

  • Talend

    Talend is a comprehensive data integration platform that offers a wide range of ETL capabilities. It provides a visual interface for designing data pipelines and supports various connectors for integrating with different data sources and destinations. Talend’s user-friendly interface and extensive feature set make it a popular choice for ETL development.

  • Apache NiFi

    Apache NiFi is an open-source data integration platform that enables the automation of data flows between systems. It offers a web-based user interface for designing and managing data pipelines, with support for data routing, transformation, and mediation. NiFi’s ease of use and flexibility make it a preferred choice for building data pipelines with complex routing and transformation requirements.

  • Docker

    Docker is a popular containerization platform that allows for easy deployment and scaling of data pipeline applications. By packaging applications and their dependencies into containers, Docker enables consistent and reproducible pipeline deployments across different environments. Docker’s lightweight nature and scalability make it ideal for deploying data pipeline applications in a distributed manner.

Soft skills of a Data Pipelines (ETL) Developer

Soft skills are essential for Data Pipelines (ETL) Developers as they play a crucial role in effectively managing and transforming data. Here are the key soft skills required at different levels of expertise:


  • Attention to Detail: Demonstrating meticulousness to ensure accuracy and reliability of data transformations.
  • Problem-Solving: Ability to identify and resolve issues that arise during the data pipeline process.
  • Communication: Effectively conveying information and collaborating with team members to ensure smooth data flow.
  • Time Management: Efficiently managing time to meet project deadlines and deliver quality results.
  • Adaptability: Being flexible and open to learning new technologies and techniques in the evolving data landscape.


  • Data Analysis: Proficiency in analyzing data patterns and trends to optimize the performance and efficiency of data pipelines.
  • Collaboration: Working closely with cross-functional teams, such as data engineers and business analysts, to align data pipeline requirements with business objectives.
  • Leadership: Taking ownership of projects, guiding junior team members, and ensuring the successful execution of data pipeline tasks.
  • Documentation: Maintaining thorough documentation of data pipeline processes, ensuring transparency and knowledge sharing within the team.
  • Problem Management: Effectively managing and resolving complex issues that may arise during the data pipeline process.
  • Continuous Learning: Keeping up-to-date with the latest advancements in data pipeline technologies and methodologies.
  • Quality Assurance: Implementing rigorous testing and validation processes to ensure the accuracy and integrity of data transformations.


  • Strategic Thinking: Developing long-term data pipeline strategies aligned with organizational goals and objectives.
  • Project Management: Overseeing multiple data pipeline projects, coordinating resources, and ensuring successful project delivery.
  • Mentorship: Mentoring and guiding junior and middle-level developers, fostering their professional growth.
  • Stakeholder Management: Effectively communicating and managing expectations of stakeholders, such as business leaders and data consumers.
  • Innovation: Identifying and implementing innovative approaches and technologies to enhance the efficiency and effectiveness of data pipelines.
  • Process Optimization: Continuously improving data pipeline processes to maximize efficiency and minimize errors.
  • Risk Management: Proactively identifying and mitigating potential risks to data integrity and pipeline performance.
  • Business Acumen: Understanding the business operations and requirements to translate them into effective data pipeline solutions.

Expert/Team Lead

  • Strategic Planning: Developing a comprehensive roadmap for data pipeline initiatives, aligning them with overall business and data strategies.
  • Team Management: Leading and managing a team of data pipeline developers, assigning tasks, and fostering a collaborative work environment.
  • Executive Communication: Presenting data pipeline strategies, progress, and outcomes to executive-level stakeholders.
  • Thought Leadership: Contributing to industry forums, publishing whitepapers, and sharing expertise to drive innovation in data pipeline practices.
  • Enterprise Integration: Collaborating with other teams, such as data governance and security, to ensure seamless integration of data pipeline processes.
  • Strategic Partnerships: Establishing partnerships with external vendors and technology providers to leverage cutting-edge tools and solutions for data pipelines.
  • Performance Optimization: Continuously optimizing data pipeline performance, scalability, and reliability in large-scale enterprise environments.
  • Change Management: Leading organizational change initiatives related to data pipeline technologies and processes.
  • Regulatory Compliance: Ensuring data pipelines adhere to regulatory requirements and data privacy regulations.
  • Business Strategy Alignment: Aligning data pipeline initiatives with the overall business strategy to drive competitive advantage and growth.
  • Continuous Improvement: Driving a culture of continuous improvement within the data pipeline team, fostering innovation and efficiency.

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 senior developers with coding and debugging, learning and implementing best practices, participating in code reviews, and contributing to small tasks within a project.$50,000 – $70,000
Middle2-5 yearsDeveloping and maintaining software applications, writing and debugging code, collaborating with cross-functional teams, participating in technical discussions, and taking on more complex tasks under the guidance of senior developers.$70,000 – $90,000
Senior5-10 yearsLeading software development projects, designing and implementing complex software solutions, mentoring junior and middle developers, conducting code reviews, providing technical guidance, and collaborating with stakeholders to define project requirements.$90,000 – $120,000
Expert/Team Lead10+ yearsLeading development teams, setting technical direction, architecting scalable solutions, managing project timelines and resources, mentoring and coaching team members, conducting performance evaluations, and driving innovation and process improvements.$120,000 – $150,000+

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