Key Responsibilities and Required Skills for Hadoop Developer

🎯 Role Definition

The Hadoop Developer is a seasoned data engineering professional responsible for designing, implementing and maintaining large‐scale distributed data processing systems using the Hadoop ecosystem. This role works closely with data scientists, analysts, architecture and operations teams to build data lakes, ETL pipelines, real‑time and batch processing frameworks, optimise performance, ensure data governance, and deliver actionable insights from massive and varied datasets.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Engineer or ETL Developer with big‑data exposure
Software Developer (Java/Scala/Python) transitioning into big‑data
Hadoop Platform Administrator moving into development focus

Advancement To:

Senior Hadoop Developer / Big Data Engineer Lead
Principal Data Platform Architect / Big Data Architect
Head of Data Engineering / Director – Big Data Platforms

Lateral Moves:

Spark/Streaming Engineer (real‑time data)
Machine Learning Engineer specializing in big‑data models
Cloud Data Platform Engineer (Data Lakehouse, Snowflake, etc)

Core Responsibilities

Primary Functions

Design, build and deploy robust big data processing solutions leveraging Hadoop ecosystem components such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark and Kafka to support enterprise analytical and operational use‑cases.
Develop and maintain scalable data pipelines for batch and real‑time ingestion, transformation and storage of structured, semi‑structured and unstructured data from multiple sources into Hadoop and downstream systems.
Configure, administer and optimise Hadoop clusters: install, configure and maintain Hadoop distributions (Cloudera, Hortonworks, MapR), monitor cluster health, tune resource usage and ensure high availability and fault tolerance.
Write, review and optimise complex queries, HiveQL, Pig scripts, MapReduce and Spark jobs: identify bottlenecks, tune performance of data workflows, optimise file formats (Parquet, Avro, ORC) and manage table partitioning and indexing.
Collaborate with data scientists and analysts to understand data requirements, develop data models and schemas, design star and snowflake models, organise data marts and maintain metadata frameworks for efficient analytics.
Build and integrate data ingestion interfaces and ETL workflows using tools such as Sqoop, Flume, Oozie, NiFi and custom scripts to load and process multi‑terabyte datasets efficiently.
Monitor, troubleshoot and debug production data workflows and Hadoop applications: analyse log files, handle node failures, driver or job failures, and implement preventive and corrective actions to maintain system stability.
Ensure data quality, data consistency and data governance: implement auditing, validation checks, error handling, reconciliation of data loads, and meet regulatory/compliance standards (e.g., GDPR) for data lakes and data warehouses.
Participate in architecture and design reviews: propose big data frameworks, define best practices and standards, drive reusable components, and contribute to roadmap for Hadoop and big‑data services.
Implement security measures and access control for Hadoop systems: configure HDFS encryption zones, Kerberos authentication, role‑based access, and ensure compliance with corporate security policies and industry standards.
Mentor and coach junior data engineers, provide code reviews, share knowledge of Hadoop ecosystem technologies, and promote team productivity and technical excellence.
Maintain version control (Git), build automation, continuous integration and delivery for big‑data applications: manage development lifecycle, coordinate releases, support multi‑environment deployments (DEV/QA/PROD).
Optimize data storage solutions: selecting file formats, compression, partitioning strategies and lifecycle management of data in HDFS and downstream storage to enhance performance and cost‑efficiency.
Participate in development of dashboards, metrics and monitoring systems: track job performance, throughput, resource utilisation, data latency, error rates and provide insights to management for decision making.
Assist in data migration, archival and consolidation projects: perform data movement from legacy systems/mainframes, restructure data, support transitions to Hadoop or modern big‑data platforms.
Collaborate with DevOps and infrastructure teams to deploy Hadoop and big‑data services in cloud or hybrid environments: provision clusters, manage configuration, containerisation and orchestration when required.
Stay current with emerging big‑data technologies, evaluate new Hadoop ecosystem components (e.g., Spark Structured Streaming, Delta Lake, Lakehouse) and drive adoption of innovations that improve scalability and reliability.
Create comprehensive technical documentation: system design specs, data flow diagrams, job scheduling diagrams, technical runbooks, operational hand‑off documents and best practice guides.
Liaise with business stakeholders and domain teams to define data product requirements, set delivery timelines, prioritize backlog and ensure alignment of data engineering deliverables to business value.
Participate in on‑call rotations or support escalations for critical data infrastructure: respond to incidents, coordinate multi‑team resolution, implement root‑cause prevention and maintain service level objectives.

Secondary Functions

Support ad‑hoc data requests and exploratory data analysis to assist business units in deriving insights from big‑data systems.
Contribute to the organisation’s data engineering roadmap by aligning Hadoop platform enhancements with business strategy, scalability goals and cost‑efficiency targets.
Collaborate across business units to translate analytics or data science requirements into engineering deliverables and pipeline designs.
Participate in agile ceremonies (sprint planning, stand‑ups, retrospectives) within the data engineering team to ensure effective planning, tracking and delivery of Hadoop development efforts.

Required Skills & Competencies

Hard Skills (Technical)

Expertise in Hadoop ecosystem: HDFS, YARN, MapReduce, Hive, Pig, HBase and other related big‑data technologies.
Proficiency in programming languages: Java, Scala or Python for big‑data development and writing MapReduce, Spark and Pig scripts.
Experience with Spark and streaming frameworks for real‑time data ingestion and processing.
Skilled in ETL/ELT tools and ingestion frameworks: Sqoop, Flume, Oozie, NiFi for data movement into Hadoop.
Proficient with SQL and NoSQL databases, data modelling and optimisation of big‑data queries and schema design.
Experience with performance tuning: file formats (Parquet, Avro), compression, partitioning, query optimisation and resource management.
Knowledge of cluster administration or working with Hadoop cluster tools (Cloudera Manager, Ambari), machine/node scaling, monitoring and operation.
Familiarity with cloud or hybrid big‑data infrastructure: provisioning, containerisation, orchestration, cost and region optimisation.
Version control, CI/CD, development lifecycle, build and deployment practices for data engineering projects.
Good understanding of data governance, security, access control, compliance frameworks and metadata management in big‑data systems.

Soft Skills

Excellent analytical and problem‑solving skills: ability to examine large and complex datasets, identify patterns, root‑cause data issues and propose technical solutions.
Strong communication and collaboration: able to work with data science, analytics, operations, product teams and translate business requirements into technical engineering tasks.
Effective time‑management and prioritisation: manage multiple concurrent jobs, deadlines, pipeline enhancements and maintenance efforts in a dynamic environment.
Attention to detail and quality‑orientation: ensure reliability, data integrity, documentation quality, test coverage and robust code in production systems.
Adaptability and continuous‑learning mindset: comfortable with evolving big‑data technologies, ecosystem upgrades, migrations and changing business needs.
Mentorship and team‑oriented mindset: support junior engineers, share knowledge of big‑data best practices, conduct code reviews and promote team excellence.
Ownership and accountability: take responsibility for data platform deliverables, performance, stability, availability and business impact.
Strategic thinking and business awareness: understand how large‑scale data solutions align to organisational goals, analytics strategies and value‑creation.
Collaboration across silos: work seamlessly with infrastructure, operations, business and data teams to implement end‑to‑end solutions.
Resilience under pressure: manage production incidents, respond to escalations, prioritise fixes and ensure service continuity.

Education & Experience

Educational Background

Minimum Education:
Bachelor’s degree in Computer Science, Software Engineering, Data Science, Information Systems or related technical field.
Preferred Education:
Master’s degree or advanced certification in Big Data technologies, Data Engineering or distributed systems.
Relevant Fields of Study:

Computer Science / Software Engineering
Data Science / Big Data Technologies
Information Systems / Analytics Engineering
Software Engineering / Distributed Systems

Experience Requirements

Typical Experience Range:
3‑5 years of hands‑on experience in Hadoop ecosystem development, data engineering or big‑data infrastructure roles.
Preferred:
5+ years of experience developing and delivering production big‑data pipelines, operating enterprise Hadoop clusters and mentoring other engineers in big‑data technologies.