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Eunice, Mueni

Nairobi, Kenya
Last Updated: 4 Jul 2023

An expert in commerce and operations management

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Core Java

Core Java

Media Marketing & Sales

Media Marketing & Sales

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Artem B., Flutter developer

Kharkiv, Ukraine
Last Updated: 18 Sep 2023

- Experienced Flutter developer with over 6 years of experience in software development. - Proficient in using Flutter and various related technologies such as Firebase Realtime Database, Firebase Cloud Firestore, Firebase Auth, Google Auth, Facebook Auth, Firebase Cloud Storage, Crashlytics, Analytics, Push Notifications, Background Location, Localization, Google Maps, Stripe, REST API, AWS S3, BloC, MVVM, Flutter Web, and Git flow. - Strong background in computer science with a Specialist's degree from "O.M.Beketov" National University of Urban Economic and Java Core courses at JavaRush. - Skilled in both team collaboration and independent work, with a track record of creating applications from scratch and implementing functions according to design requirements. - Experienced in developing cross-platform mobile applications using Flutter and React-Native. - Familiar with various development methodologies, principles, paradigms, and patterns. - Proficient in SDLC and software development practices. - Strong technical expertise in mobile app development, particularly in the domains of crypto wallets, credit management, Bluetooth device monitoring, housing services management, charity platforms, and location tracking. - Proven ability to deliver high-quality software solutions and achieve results in a timely manner.

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Flutter   6 yr.

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Yehor Sh., Java Software Engineer

Last Updated: 4 Jul 2023

- 5 years of experience in the IT industry working with Java; - Experience working with banking and financial projects, fintech sphere; - Good abilities with SQL, MySQL, PostgreSQL, and RabbitMQ; - Experience with AWS and Google API; - Good knowledge of C++ and JavaScript; - Good abilities working with PayPal API; - Deep skills working with Docker; - Have experience in programming Spring Framework; - Practice with Elasticsearch (Kibana); - Upper-Intermediate English.

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Taras Z., Android Software Engineer

Last Updated: 4 Jul 2023

- Having over 7+ years of experience in developing native mobile and TV applications for the Android platforms - Deep knowledge of the Android Framework significantly increased my technical and soft skills. - Successfully accomplished above seven long-term projects at the middle, senior, and team-lead developer positions, and tech consultant. - Also participated in many short-term, bug fixing, feature adding, and library developing projects. - Have strong mathematical and computer science background - Upper-Intermediate English - Available ASAP

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Java   8 yr.


Kotlin   3 yr.

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Yehor K., Senior Java Engineer

Last Updated: 4 Jul 2023

- Have 5 years experience with Java and related technology; - Experience working with Docker; - Deep skills with PostgreSQL, MySQL, MS SQL Server; - Experience working with financial projects; - Good knowledge of PayPal API; - Experience working with AWS and Google API; - Experience with Microservices; - Good logical thinking, self-learning, high level of responsibility; - Responsible, hard-working, result-oriented, creative and communicative, team player; - Upper-Intermediate English.

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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.

Hire Analytics Developer as Effortless as Calling a Taxi

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FAQs about Analytics Development

How do I hire a Analytics developer? Arrow

If you urgently need a verified and qualified Analytics developer, and resources for finding the right candidate are lacking, UPSTAFF is exactly the service you need. We approach the selection of Analytics 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.

Where is the best place to find Analytics developers? Arrow

Undoubtedly, there are dozens, if not hundreds, of specialized services and platforms on the network for finding the right Analytics 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.

How are Upstaff Analytics developers different? Arrow

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 Analytics candidates for each job position. We are fueled by a passion for technical expertise, drawn from our deep understanding of the industry.

How quickly can I hire Analytics developers through Upstaff? Arrow

Our journey starts with a 30-minute discovery call to explore your project challenges, technical needs, and team diversity. Meet Carefully Matched Analytics 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 Analytics 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 Analytics 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 Analytics 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 Analytics instruments and tools?

  • Google Analytics: Google Analytics is one of the most widely used analytics tools in the world. It was launched in 2005 and has become the go-to tool for tracking website traffic, user behavior, and conversion rates. With its robust features and easy-to-use interface, it provides valuable insights into website performance and helps businesses make data-driven decisions.
  • Adobe Analytics: Adobe Analytics, formerly known as Omniture, is a comprehensive analytics platform that offers advanced data visualizations and real-time reporting. It has been around since 1996 and has a strong presence in the enterprise market. Adobe Analytics provides deep insights into customer behavior, enabling businesses to optimize their marketing strategies and drive growth.
  • IBM Watson Analytics: IBM Watson Analytics is an AI-powered analytics tool that enables users to uncover patterns and trends in data through natural language processing. It was introduced in 2014 and has gained popularity for its ability to analyze complex data sets and generate actionable insights. IBM Watson Analytics is particularly useful for businesses looking to leverage AI technology to drive innovation and improve decision-making.
  • Tableau: Tableau is a powerful data visualization and business intelligence tool that allows users to create interactive dashboards and reports. It was founded in 2003 and has revolutionized the way organizations analyze and present data. Tableau’s intuitive interface and extensive range of visualization options make it a favorite among data analysts and business users alike.
  • Microsoft Power BI: Microsoft Power BI is a cloud-based analytics tool that enables users to create interactive reports and dashboards. It was released in 2015 and has gained traction due to its seamless integration with other Microsoft products and services. With its user-friendly interface and robust data modeling capabilities, Power BI empowers organizations to extract valuable insights from their data and share them across the enterprise.
  • Splunk: Splunk is a leading platform for analyzing machine-generated data, such as log files and network traffic. It was introduced in 2003 and has become a go-to tool for IT operations and security teams. Splunk’s powerful search and visualization capabilities help organizations gain visibility into their IT infrastructure and detect potential issues or security threats.
  • RapidMiner: RapidMiner is an open-source data science platform that provides a range of tools for data preparation, modeling, and deployment. It was first released in 2001 and has gained popularity among data scientists and analysts for its ease of use and powerful predictive analytics capabilities. RapidMiner’s drag-and-drop interface and extensive library of machine learning algorithms make it a valuable tool for organizations looking to leverage data science for business insights.

How and where is Analytics used?

Case NameCase Description
1. Fraud DetectionAnalytics development plays a crucial role in detecting and preventing fraudulent activities. By analyzing patterns and anomalies in large datasets, it can identify suspicious transactions, unauthorized access, or fraudulent behavior. This helps businesses protect their financial assets, prevent losses, and maintain trust with their customers.
2. Customer SegmentationAnalytics development enables businesses to categorize their customer base into distinct segments based on various factors such as demographics, behavior, preferences, and purchasing patterns. This segmentation helps in creating targeted marketing campaigns, personalized product recommendations, and better understanding customer needs, ultimately leading to improved customer satisfaction and increased sales.
3. Supply Chain OptimizationBy leveraging analytics development, organizations can optimize their supply chain operations. It helps in forecasting demand, managing inventory levels, identifying bottlenecks, and optimizing logistics. This leads to improved efficiency, reduced costs, faster order fulfillment, and better customer service.
4. Predictive MaintenanceAnalytics development is instrumental in implementing predictive maintenance strategies. By analyzing historical data, sensor readings, and equipment performance metrics, it can predict when equipment or machinery is likely to fail. This enables proactive maintenance scheduling, minimizing downtime, reducing maintenance costs, and maximizing equipment lifespan.
5. Healthcare AnalyticsAnalytics development in healthcare helps in improving patient outcomes, optimizing resource allocation, and enhancing operational efficiency. It can analyze patient data, medical records, and treatment outcomes to identify trends, predict disease progression, and recommend personalized treatment plans. This leads to better decision-making, reduced healthcare costs, and improved patient care.
6. Sentiment AnalysisAnalytics development enables sentiment analysis by analyzing text data from various sources such as social media, customer reviews, and surveys. It helps businesses understand public opinion, customer satisfaction levels, and brand sentiment. This information can be used to make informed business decisions, improve products and services, and enhance customer experience.
7. Churn PredictionAnalytics development can predict customer churn by analyzing historical customer data, usage patterns, and behavior. By identifying customers at risk of leaving, businesses can implement targeted retention strategies such as personalized offers, proactive customer support, or loyalty programs. This helps in reducing customer churn, increasing customer loyalty, and improving overall business performance.
8. Energy ManagementAnalytics development plays a vital role in energy management by analyzing energy consumption patterns, identifying energy-saving opportunities, and optimizing energy usage. It helps businesses monitor and control energy usage in real-time, identify inefficiencies, and implement energy conservation measures. This leads to reduced energy costs, improved sustainability, and environmental benefits.
9. Risk AssessmentAnalytics development assists in risk assessment by analyzing historical data, market trends, and external factors. It helps businesses evaluate potential risks, calculate risk scores, and develop risk mitigation strategies. This enables proactive risk management, better decision-making, and protection against financial losses or reputational damage.

Cases when Analytics does not work

  1. Insufficient or poor-quality data: Analytics heavily relies on data to generate insights and make accurate predictions. If the data being used is incomplete, inaccurate, or of poor quality, it can significantly impact the effectiveness of analytics. Without reliable data, the analytics models may produce misleading or incorrect results.
  2. Lack of clear objectives: Analytics is most effective when there are clear objectives and goals in place. If the purpose of the analysis is not well-defined or if the desired outcomes are ambiguous, it becomes challenging to derive meaningful insights from the data. Without a clear focus, analytics may fail to provide actionable recommendations.
  3. Inadequate expertise and skills: Analytics requires a certain level of expertise and skills to interpret and analyze complex data sets. If the individuals involved in the analytics process lack the necessary knowledge or experience, they may struggle to extract valuable insights from the data. In such cases, analytics may not deliver the expected results due to a lack of competent resources.
  4. Biased or skewed data: Analytics models are only as good as the data they are trained on. If the training data is biased or skewed, the resulting insights may be similarly biased or skewed. This can lead to incorrect conclusions and recommendations. Ensuring that the data used for analytics is representative and unbiased is crucial for accurate results.
  5. Unforeseen events or outliers: Analytics models are typically built based on historical data and trends. However, they may not always account for unforeseen events or outliers that can significantly impact the analysis. Sudden market disruptions, natural disasters, or other unexpected occurrences can render the analytics models ineffective or inaccurate.
  6. Overreliance on analytics: While analytics can provide valuable insights, it is important to remember that it is just one tool in the decision-making process. Overreliance on analytics without considering other factors such as intuition, domain expertise, and real-time information can limit the effectiveness of the analysis. Human judgment should still play a role in decision-making to avoid potential blind spots or oversights.

TOP 11 Facts about Analytics

  • Analytics is the systematic analysis of data or statistics to derive meaningful insights and make informed decisions.
  • According to a report by IBM, the global analytics market is estimated to reach $105.8 billion by 2024, growing at a CAGR of 25.4% from 2019 to 2024.
  • In 2018, the top three industries implementing analytics solutions were financial services, manufacturing, and healthcare, accounting for nearly 50% of the total market share.
  • Data analytics can help businesses identify trends, patterns, and correlations to optimize operations, improve customer experiences, and drive revenue growth.
  • According to a study by McKinsey, organizations that extensively use customer analytics are more likely to outperform their competitors in terms of revenue growth and profitability.
  • Descriptive analytics focuses on understanding what has happened in the past by analyzing historical data, providing valuable insights into trends and patterns.
  • Predictive analytics uses statistical models and machine learning algorithms to forecast future outcomes based on historical data, enabling organizations to make proactive decisions.
  • Prescriptive analytics recommends specific actions to optimize outcomes based on a combination of historical data, real-time insights, and business rules.
  • Big data analytics involves processing and analyzing large and complex datasets to extract valuable insights that traditional analytics methods may not be able to uncover.
  • Data visualization plays a crucial role in analytics by presenting complex data in a visual format, making it easier for stakeholders to comprehend and interpret the information.
  • As the volume of data continues to grow exponentially, the demand for skilled analytics professionals is also increasing. According to the U.S. Bureau of Labor Statistics, the employment of operations research analysts, which includes analytics professionals, is projected to grow 25% from 2019 to 2029, much faster than the average for all occupations.

Pros & cons of Analytics

9 Pros of Analytics

  • Improved decision making: Analytics provides businesses with valuable insights and data-driven information that can help in making informed decisions.
  • Identifying patterns and trends: By analyzing large volumes of data, analytics can identify patterns and trends that may not be apparent through manual analysis.
  • Increased efficiency: Analytics can automate processes and streamline operations, leading to increased efficiency and productivity.
  • Competitive advantage: By leveraging analytics, businesses can gain a competitive edge by identifying opportunities and making strategic decisions based on data.
  • Better customer understanding: Analytics allows businesses to gain a deeper understanding of their customers’ preferences, behavior, and needs, enabling personalized marketing and improved customer satisfaction.
  • Risk mitigation: Analytics can help identify potential risks and predict outcomes, allowing businesses to proactively mitigate risks and minimize losses.
  • Optimized resource allocation: By analyzing data, businesses can allocate their resources more effectively, optimizing budget allocation, workforce planning, and inventory management.
  • Improved marketing effectiveness: Analytics can provide insights into marketing campaigns, helping businesses optimize their marketing strategies and target the right audience with personalized messages.
  • Innovation and product development: Analytics can uncover insights that drive innovation and support product development by identifying market trends, customer preferences, and areas for improvement.

9 Cons of Analytics

  • Data privacy concerns: The use of analytics involves handling large volumes of data, which raises concerns about data privacy and security.
  • Complexity and learning curve: Implementing analytics solutions and analyzing data can be complex, requiring skilled professionals and a learning curve for businesses.
  • Cost of implementation: Analytics solutions can be costly to implement, including infrastructure, software, and training costs.
  • Data quality issues: Poor data quality can lead to inaccurate insights and decisions. Ensuring data quality is a challenge in analytics implementation.
  • Dependency on data availability: Analytics heavily relies on the availability of quality data. Incomplete or inaccurate data can limit the effectiveness of analytics initiatives.
  • Overemphasis on data: Relying solely on data-driven decision making can overlook qualitative factors and human intuition, potentially leading to suboptimal outcomes.
  • Potential for bias: Analytics algorithms can be biased if not carefully designed and monitored, leading to biased insights and decisions.
  • Integration challenges: Integrating analytics systems with existing infrastructure and software can pose challenges and require technical expertise.
  • Resistance to change: Implementing analytics may face resistance from employees who are not accustomed to data-driven decision making or fear job automation.

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

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior Developer0-2 yearsAssisting in the development of software applications, debugging and testing code, learning programming languages and frameworks, collaborating with team members on small-scale projects.$50,000 – $70,000
Middle Developer2-5 yearsDeveloping and maintaining software applications, writing clean and efficient code, participating in code reviews, collaborating with team members on medium-sized projects, assisting junior developers, and gaining expertise in specific technologies.$70,000 – $90,000
Senior Developer5-8 yearsLeading software development projects, designing and implementing complex software solutions, mentoring junior and middle developers, participating in architectural decisions, optimizing code performance, and collaborating with cross-functional teams.$90,000 – $120,000
Expert/Team Lead Developer8+ yearsLeading and managing software development teams, providing technical guidance and mentoring to team members, overseeing project timelines and deliverables, collaborating with stakeholders, making strategic technical decisions, and ensuring high-quality code and project success.$120,000 – $150,000+

Soft skills of a Analytics Developer

Soft skills are essential for an Analytics Developer to excel in their role, as they not only need technical expertise but also the ability to effectively communicate and collaborate with others. Here are the soft skills required at different levels of experience:


  • Attention to Detail: Paying close attention to data and ensuring accuracy in analysis.
  • Problem Solving: Identifying and resolving issues to ensure smooth data processing.
  • Teamwork: Working collaboratively with team members to achieve project goals.
  • Communication: Clearly conveying complex technical concepts to non-technical stakeholders.
  • Time Management: Efficiently managing tasks and meeting project deadlines.


  • Leadership: Taking charge of smaller projects and guiding junior team members.
  • Critical Thinking: Analyzing data to identify patterns, trends, and insights.
  • Adaptability: Being flexible and open to new technologies and approaches.
  • Client Management: Interacting with clients and understanding their requirements.
  • Presentation Skills: Effectively presenting data analysis findings to stakeholders.
  • Conflict Resolution: Resolving conflicts within the team to maintain a positive work environment.
  • Continuous Learning: Keeping up-to-date with advancements in analytics tools and techniques.


  • Strategic Thinking: Developing long-term data strategies and goals for the organization.
  • Mentoring: Guiding and mentoring junior and middle-level analytics developers.
  • Project Management: Overseeing multiple projects and ensuring their successful completion.
  • Business Acumen: Understanding the business objectives and aligning analytics efforts accordingly.
  • Collaboration: Working closely with cross-functional teams to achieve business objectives.
  • Influence: Influencing decision-making through data-driven insights and recommendations.
  • Data Visualization: Presenting complex data in a visually appealing and understandable manner.
  • Client Relationship Management: Building and maintaining strong client relationships.

Expert/Team Lead

  • Strategic Leadership: Providing strategic direction to the analytics team.
  • Business Development: Identifying opportunities for business growth and expansion.
  • Thought Leadership: Contributing to industry knowledge through research and publications.
  • Change Management: Leading the team through organizational changes and transitions.
  • Executive Communication: Effectively communicating with top-level executives and stakeholders.
  • Team Building: Building and nurturing a high-performing analytics team.
  • Conflict Resolution: Resolving conflicts at a team and organizational level.
  • Innovation: Driving innovation in analytics processes and methodologies.
  • Quality Assurance: Ensuring the accuracy and reliability of analytics deliverables.
  • Strategic Partnerships: Collaborating with external partners to enhance analytics capabilities.
  • Decision Making: Making informed decisions based on data analysis and insights.

TOP 10 Analytics Related Technologies

  • Python

    Python is one of the most popular programming languages for analytics software development. It offers a wide range of libraries and frameworks such as Pandas, NumPy, and SciPy, which provide powerful tools for data manipulation, analysis, and visualization. Python’s simplicity and readability make it a preferred choice for both beginners and experienced developers.

  • R

    R is another widely used programming language for statistical computing and graphics. It has a vast ecosystem of packages specifically designed for data analysis, making it a popular choice among statisticians and data scientists. R provides extensive support for exploratory data analysis, statistical modeling, and visualization.

  • SQL

    SQL (Structured Query Language) is essential for working with relational databases, which are commonly used to store and manage large datasets. It allows developers to query, manipulate, and retrieve data efficiently. SQL is a fundamental skill for anyone involved in analytics software development.

  • Apache Hadoop

    Apache Hadoop is an open-source framework that enables distributed processing of large datasets across clusters of computers. It provides a scalable and fault-tolerant environment for storing and analyzing big data. Hadoop’s MapReduce programming model and HDFS (Hadoop Distributed File System) are widely used for big data analytics.

  • Apache Spark

    Apache Spark is a fast and general-purpose cluster computing system that provides in-memory data processing capabilities. It offers high-level APIs in Java, Scala, Python, and R, making it accessible to a wide range of developers. Spark is known for its speed and scalability, making it suitable for large-scale data processing and machine learning tasks.

  • TensorFlow

    TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem for building and deploying machine learning models. TensorFlow’s flexibility, scalability, and extensive community support have made it one of the most popular choices for implementing deep learning algorithms and neural networks.

  • Tableau

    Tableau is a leading data visualization tool that allows users to create interactive and visually appealing dashboards, reports, and charts. It offers a drag-and-drop interface, making it easy for non-technical users to explore and analyze data. Tableau integrates well with various data sources and supports real-time data analysis.

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