A Hadoop Developer is a professional who specializes in developing and maintaining applications that utilize the Hadoop ecosystem. Hadoop is an open-source framework that enables distributed processing and storage of large datasets across clusters of computers. Hadoop Developers play a crucial role in designing, coding, and optimizing data processing workflows within this framework. In this blog post, we will describe the primary responsibilities and the most in-demand hard and soft skills for Hadoop Developers.

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Main Responsibilities of a Hadoop Developer

The following list describes the typical responsibilities of a Hadoop Developer:

Analyze

• Analyze and optimize data workflows for scalability and efficiency.

• Analyze test reports, identifies any test issues / errors.

Assist with

• Assist team with resolving technical complexities involved in realizing story work.

• Assist with Data Architect with Data Modeling.

Benchmark

Benchmark systems, analyze system bottlenecks, and propose solutions to eliminate them.

Build

• Build and incorporate automated unit tests, participate in integration testing efforts.

• Build and manage workflows using tools like Apache Oozie or Apache Airflow.

• Build high performing data models on big-data architecture as data services.

• Build and test highly performant and scalable enterprise-grade ETL pipelines on Hadoop platform.

• Build tools to automate, provision, deploy, monitor and manage production systems.

• Build utilities, user defined functions, and frameworks to better enable data flow patterns.

Collaborate with

• Collaborate with cross-functional teams to integrate Hadoop solutions.

• Collaborate with data scientists and analysts to understand data requirements.

Conduct

• Conduct data analysis and feature engineering as part of the ML development lifecycle.

• Conduct vendor product gap analysis / comparison.

Consult with

Consult with other IT Developers, Business Analysts, Systems Analysts, Project Managers and vendors.

Contribute to

• Contribute to existing test suites (integration, regression, performance).

• Contribute to story refinement / defining requirements.

• Contribute to the determination of technical and operational feasibility of solutions.

• Contribute to the overall system design and operational design.

Coordinate

Coordinate regional deployments and testing.

Create

• Create and document the tests necessary to ensure that an application meets performance requirements.

• Create and maintain data ingestion and extraction processes.

• Create Conceptual Data Model, Data Flows, Data Management specifications.

• Create documentation to support knowledge sharing.

Debug

Debug and resolve issues related to data processing or job failures.

Define

Define and build data acquisitions and consumption strategies.

Deliver

Deliver the data services on container-based architecture such as Kubernetes and Docker.

Design

• Design and build the data services on container-based architecture such as Kubernetes and Docker.

• Design and develop data processing applications using Hadoop technologies.

• Design and develop test plan and test cases to validate and optimize ML models.

• Design and implement best practice guidelines for each application.

• Design and implement data partitioning and sharding strategies.

• Design overall approach to GFCR data lake including ingestion and consumption layers.

Develop

• Develop, analyze and maintain all ETL jobs according to Mayo standards on Sqoop.

• Develop and design new data processing workflows using SSIS.

• Develop and design new reports using SSRS.

• Develop and maintain data schemas and data models.

• Develop and maintain data streaming and real-time processing applications.

• Develop and maintain documentation for Hadoop applications and processes.

• Develop and maintain ETL (Extract, Transform, Load) processes.

• Develop and optimize MapReduce programs for distributed data processing.

• Develop Big Data Strategy and Roadmap for the Enterprise.

• Develop database solutions by designing proposed system.

• Develop, maintain and support existing applications by making modifications as require.

• Develop scalable streaming solutions based on Spark, Kafka and / or Flume.

• Develop standards, patterns, best practices for reuse and acceleration.

Document

• Document guidelines to prevent performance problems.

• Document software high level design / approach and code.

• Document test cases, test results and version control of deployments.

• Document what has to be migrated.

Drive

• Drive conceptual and logical architecture design for new initiatives.

• Drive Database Design, Development of ER models.

Ensure

• Ensure data security and privacy in compliance with regulatory requirements.

• Ensure initiatives are adhering to strategic architecture principles.

• Ensure solutions adhere to enterprise standards.

• Ensure that software is developed to meet functional, non-functional, and compliance requirements.

Evaluate

• Evaluate, analyze the problem definition and the requirements.

• Evaluate emerging technologies against business and IT strategic needs.

Exposure

Exposure to Autosys Scheduling and write JIL scripts.

Follow

Follow the software development lifecycle.

Handle

Handle multiple assignments with multiple deadlines simultaneously.

Identify

Identify integration points.

Implement

• Implement data encryption and data masking techniques.

• Implement data governance and compliance policies.

• Implement data transformation and cleansing procedures.

• Implement security measures and access controls for Hadoop applications.

Integrate

Integrate Hadoop applications with external systems or databases.

Interact with

Interact with system stakeholders for archival of old data.

Interface with

Interface with business areas to ensure all initiatives support business strategies and goals.

Lead

Lead the design and implementation plan for the organization?.

Learn

Learn new products / tools / technologies to maintain the companys competitive edge.

Leverage

Leverage expertise in processing data on a CRM integration project.

Maintain

• Maintain and develop on Apache nifi and Kafka.

• Maintain comprehensive knowledge of industry standards, methodologies, processes, and best practices.

• Maintain existing SSIS workflows and SSRS reports.

• Maintain existing Tableau dashboards.

Manage

• Manage and schedule data backup and recovery processes.

• Manage deployment & configuration.

Monitor

Monitor and tune Hadoop cluster performance and resource utilization.

Optimize

• Optimize data storage and compression techniques in Hadoop.

• Optimize data storage and retrieval processes for performance.

Own

Own and manage design and development / enhancement of Data model and database objects.

Participate in

• Participate in the continued definition of our target Strategies.

• Participate in the development of enterprise data strategy.

• Participate in the testing of developed systems / solutions.

Perform

• Perform code reviews and provide guidance to junior developers.

• Perform data migration and conversion activities.

• Perform performance testing and tuning of Hadoop applications.

• Perform proof of concept as necessary to mitigate risk or implement new ideas.

• Perform Sanity check every day / week to ensure all the applications are running good.

• Perform tuning using execution plans and other tools.

• Perform unit testing and debugging.

Prioritize

Prioritize and manage own workload in order to deliver quality results and meet timelines.

Process

Process re-engineering and system integration experience.

Provide

• Provide architectural consultation / education to the organization. '.

• Provide BAU application support to business partners and maintenance of production applications.

• Provide innovative tactical solutions when necessary, to meet regional requirements.

• Provide input on staffing, budget and personnel.

• Provide input to and drive programming standards.

• Provide oversight and guidance to our Data Engineering development team.

• Provide technical direction and frameworks to meet business needs.

• Provide technical governance to development teams.

Research

Research vendor products / alternatives.

Revert

Revert back the changes in case if needed.

Review

Review code developed by other IT Developers.

Seek

Seek clarifications and understand new product requirements from Product functional team.

Serve

Serve in the role of a Solutions Architect within Data, Analytics & Insight Technology organization.

Set

Set test conditions based upon code specifications.

Stay

Stay updated with new features and enhancements in the Hadoop ecosystem.

Suggest

Suggest best practices and implementation strategies using Hadoop, Python, Java, ETL tools.

Support

• Support data ingestion, preparation and publication capabilities.

• Support data provisioning and transformation workloads.

• Support enterprise data management methodology, associated standards and data strategies.

• Support multiple projects with competing deadlines.

• Support ongoing data management efforts for Development, QA and Production environments.

• Support performance optimizations / fine tuning process as applicable.

• Support transition of application throughout the Product Development life cycle.

Translate

Translate product requirements to conceptual design from database perspective.

Troubleshoot

• Troubleshoot and identify technical problems, in applications or processes and provide solutions.

• Troubleshoot and identify technical problems in applications processes and provide solutions.

• Troubleshoot and resolve issues related to data quality or integrity.

• Troubleshoot application performance problems.

• Troubleshoot problems in Production and lower environments.

Tune

Tune database processing to improve performance.

Understand

• Understand of security standards in Hadoop.

• Understand relevant application technologies and development life cycles.

Update

Update architecture standards and guidelines.

Utilize

Utilize a thorough understanding of available technology, tools, and existing designs.

Validate

Validate end-to-end reconciliation.

Work with

• Work closely with Business Intelligence technologies such as Qlik, Tableau, etc..

• Work closely with different teams to debug and solve production issues.

• Work closely with ETL technologies such as SyncSort, Informatica, etc.

• Work on a variety of projects and enhancements on a Hadoop platform.

• Work to establish a Hadoop Cluster architecture.

• Work under minimal supervision, with general guidance from more seasoned consultants.

• Work with Application teams to manage and track Technical debts to closure.

• Work with business partners to translate functional requirements into technical requirements.

• Work with teams to resolving operational & performance issues.

Write

• Write code for enhancing existing programs or developing new programs.

• Write code for moderately complex system designs.

• Write detailed technical specifications for subsystems.

• Write efficient Hive or Pig queries for data analysis and reporting.

• Write programs that span platforms.

Most In-demand Hard Skills

The following list describes the most required technical skills of a Hadoop Developer:

1. Proficiency in programming languages such as Java, Python, or Scala.

2. Knowledge of Hadoop distributed file system (HDFS) and data processing frameworks (e.g., Apache Spark, Apache Hive).

3. Understanding of Hadoop architecture and the MapReduce programming model.

4. Competence in SQL querying and database management.

5. Skill in working with NoSQL databases, such as HBase or Cassandra.

6. Proficiency in data serialization formats like Avro or Parquet.

7. Knowledge of data integration and ETL tools (e.g., Apache Sqoop, Apache Flume).

8. Understanding of data warehousing concepts and techniques.

9. Competence in working with Hadoop ecosystem components (e.g., Apache Kafka, Apache Storm).

10. Knowledge of cluster management and resource allocation tools (e.g., Apache YARN, Apache Mesos).

11. Skill in working with data visualization tools (e.g., Tableau, Power BI).

12. Proficiency in Linux/Unix command-line and shell scripting.

13. Understanding of data governance and data lifecycle management.

14. Competence in distributed storage systems like Apache HBase or Apache Cassandra.

15. Knowledge of machine learning and statistical analysis techniques.

16. Skill in working with cloud-based Hadoop platforms (e.g., Amazon EMR, Google Cloud Dataproc).

17. Proficiency in version control systems (e.g., Git, SVN).

18. Understanding of containerization technologies like Docker or Kubernetes.

19. Competence in performance tuning and optimization of Hadoop applications.

20. Knowledge of data security and access control mechanisms in Hadoop.

Most In-demand Soft Skills

The following list describes the most required soft skills of a Hadoop Developer:

1. Strong problem-solving and analytical abilities.

2. Excellent communication and collaboration skills.

3. Effective teamwork and the ability to work in cross-functional teams.

4. Adaptability and flexibility to changing project requirements.

5. Time management and organizational skills.

6. Attention to detail and a focus on delivering high-quality work.

7. Ability to work independently and take ownership of tasks.

8. Continuous learning mindset to keep up with evolving technologies.

9. Critical thinking and the ability to evaluate different approaches.

10. Creative thinking and the ability to find innovative solutions.

Conclusion

In conclusion, Hadoop Developers is responsible for developing and optimizing data processing applications within the Hadoop ecosystem. Their responsibilities encompass designing and implementing data workflows, integrating external systems, ensuring data quality and security, and optimizing performance. Alongside technical skills in programming, Hadoop frameworks, and data processing, soft skills such as problem-solving, communication, and adaptability are essential for success in this role.