Key Responsibilities and Required Skills for Big Data Developer

🎯 Role Definition

As a Big Data Developer, you will design, build, and maintain scalable data pipelines and platforms that enable analytics, machine learning, and business reporting. You will work with large datasets using distributed processing frameworks (e.g., Apache Spark, Hadoop), streaming systems (e.g., Kafka, Flink), and cloud-native data services (AWS/GCP/Azure) to deliver performant, reliable, and secure data solutions. This role requires strong software engineering discipline, a deep understanding of data architecture, and the ability to collaborate with data scientists, analysts, product owners, and SRE teams to turn data into actionable business outcomes.

Keywords: Big Data Developer, data engineering, Apache Spark, Hadoop, Kafka, ETL pipelines, data lake, cloud data platforms, real-time streaming, batch processing, data governance, scalable data architecture.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Engineer (Junior) with experience in batch ETL and SQL-based transformations.
Software Engineer transitioning into data platforms after building APIs and backend services.
ETL Developer or BI Developer moving from traditional data warehousing to big data technologies.

Advancement To:

Senior Big Data Developer / Lead Data Engineer — owning architecture decisions and mentoring teams.
Data Engineering Manager — managing multiple data teams and delivery roadmaps.
Data Platform Architect / Principal Engineer — designing enterprise-wide data platforms and standards.

Lateral Moves:

Machine Learning Engineer — focusing on feature engineering and model pipelines.
Data Analyst / Analytics Engineer — concentrating on analytics layers and SQL-first transformations.
DevOps / SRE for data platforms — ensuring operational excellence and platform reliability.

Core Responsibilities

Primary Functions

Design, implement, and maintain end-to-end scalable ETL/ELT pipelines using Apache Spark, Spark Streaming, or equivalent distributed processing frameworks to transform raw data into analytics-ready datasets that meet performance and reliability SLAs.
Architect and build data ingestion solutions that collect and normalize high-volume, high-velocity data from multiple sources (Kafka, Kinesis, change data capture, APIs, logs) into the data lake or data warehouse with schema evolution and fault-tolerance.
Develop and optimize batch and streaming data processing jobs to minimize latency, optimize resource consumption, and guarantee exactly-once or at-least-once processing semantics as required by product stakeholders.
Implement and enforce data partitioning, bucketing, compaction, and format strategies (Parquet/Avro/ORC) to improve query performance and storage efficiency for downstream analytics and BI consumption.
Build and maintain robust data lake and data warehouse solutions on cloud platforms (AWS: S3/Glue/EMR/Redshift; GCP: BigQuery/Dataflow; Azure: Data Lake/Databricks/Synapse) with secure access controls, cost controls, and operational monitoring.
Develop and maintain streaming architectures using Apache Kafka, Kafka Streams, Flink, or Spark Structured Streaming to enable real-time analytics, alerting, and event-driven workflows that support business-critical use cases.
Design and implement data quality frameworks, validation rules, and monitoring pipelines to automatically detect anomalies, lineage issues, schema changes, and stale or missing data.
Collaborate with data scientists and ML engineers to productionize feature engineering pipelines and model inference workflows, ensuring reproducibility, versioning, and performance in production.
Create and maintain comprehensive documentation for data schemas, pipeline designs, SLAs, and runbooks to support on-call rotations and knowledge transfer across teams.
Implement CI/CD pipelines for data engineering artifacts (Spark jobs, Airflow DAGs, SQL transforms, Helm charts) to enable repeatable deployments, automated testing, and rollback capabilities.
Tune and optimize distributed jobs, cluster configurations, and query plans to reduce compute costs and improve throughput for large-scale jobs across Hadoop, Spark, or cloud-managed platforms.
Design and implement secure data access patterns, encryption at rest and in transit, role-based access controls, and compliance controls (GDPR, CCPA, HIPAA) to protect sensitive data and meet regulatory requirements.
Integrate metadata management, data cataloguing (e.g., Apache Atlas, AWS Glue Data Catalog), and data lineage tools to increase data discoverability and help stakeholders trust the data.
Work closely with product managers and business stakeholders to translate functional requirements into technical designs, acceptance criteria, and measurable success metrics for data initiatives.
Troubleshoot production incidents, perform root cause analysis, and drive remediation and preventive actions to improve pipeline reliability and reduce recurrence.
Build reusable libraries, templated patterns, and framework components to accelerate pipeline development and standardize engineering best practices across the organization.
Convert complex SQL-heavy workflows into scalable distributed processing jobs while preserving business logic and accuracy, and validate transformed outputs against legacy systems.
Evaluate new big data technologies, frameworks, and managed services, run proofs-of-concept, and recommend pragmatic migration strategies or adoption plans aligned with business priorities.
Mentor junior engineers and collaborate within cross-functional agile teams, sharing best practices in code review, testing, observability, and performance engineering for data systems.
Manage resource provisioning, cluster lifecycle, autoscaling, and cost governance for on-prem or cloud clusters to optimize utilization and balance performance with budget expectations.
Implement event-driven, micro-batch, or hybrid processing patterns to support diverse requirements from low-latency streaming to compute-intensive batch analytics.
Ensure pipeline observability by integrating logging, distributed tracing, and business-metric emissions so product owners can track data freshness, throughput, and SLAs.
Develop backward- and forward-compatible schema migration strategies and automated tests to reduce downtime and preserve data integrity during schema evolution.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Apache Spark (Core, SQL, Structured Streaming) — build, optimize, and tune large-scale distributed jobs.
Hadoop ecosystem (HDFS, YARN, MapReduce) experience or equivalent familiarity with modern replacements (cloud-managed services).
Streaming platforms: Apache Kafka (producers, consumers, Connect), Kafka Streams, Flink, or Kinesis for real-time ingestion and processing.
Proficiency in Python (PySpark), Scala, and/or Java for building data transformation pipelines and reusable libraries.
Advanced SQL skills: complex joins, window functions, CTEs, query optimization and profiling for large datasets.
Cloud data platforms: AWS (S3, EMR, Glue, Redshift, Athena), GCP (BigQuery, Dataflow, Dataproc), or Azure (Databricks, Synapse) — design and operate cloud-native data solutions.
Data modeling for analytical systems: star/snowflake schemas, dimensional modeling, and denormalization strategies for performance.
Data storage formats and optimization: Parquet, Avro, ORC, partitioning, compaction, and compression techniques.
Workflow orchestration tools: Apache Airflow, Luigi, or cloud-native schedulers for ETL/ELT pipeline management.
CI/CD for data engineering: Git, Jenkins/GitHub Actions/GitLab, automated tests, containerization (Docker), and Kubernetes basics for deployments.
Data governance, metadata and catalog tools: lineage, ER/metadata management, data quality frameworks.
Monitoring & observability: Prometheus, Grafana, ELK/EFK, and alerting for pipeline health and SLA tracking.
Experience with NoSQL databases (Cassandra, HBase, MongoDB) and columnar warehouses (Redshift, Snowflake, BigQuery) where applicable.
Experience implementing schema evolution, CDC (Debezium), and integration with relational systems and microservices.

Soft Skills

Strong written and verbal communication skills to explain complex technical designs to non-technical stakeholders, document solutions, and produce runbooks.
Problem-solving mindset with a customer-first approach to prioritize data reliability and timely delivery of high-impact features.
Collaborative team player who can partner with data scientists, analysts, product managers, and SREs in cross-functional environments.
Mentorship and leadership aptitude to coach junior engineers and promote best practices in code quality and testing.
Time management and organizational skills to balance multiple priorities, technical debt, and delivery commitments.
Adaptability and continuous learning orientation to evaluate and adopt new big data technologies and patterns.
Detail-oriented mindset for implementing thorough data validation, unit/integration tests, and quality checks.
Strong analytical thinking and ability to translate business questions into performant data solutions.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Software Engineering, Information Systems, Mathematics, Statistics, or a closely related technical field.

Preferred Education:

Master's degree in Computer Science, Data Science, Artificial Intelligence, or related discipline.
Professional certifications (AWS Big Data, Google Cloud Professional Data Engineer, Databricks, Confluent) are a strong plus.

Relevant Fields of Study:

Computer Science / Software Engineering
Data Science / Applied Mathematics
Information Systems / Data Engineering

Experience Requirements

Typical Experience Range: 3–8+ years building and operating data processing systems; mid-level roles typically 3–5 years, senior roles 5+ years.

Preferred:

Proven track record delivering production-grade big data pipelines and platforms for analytics or ML use cases.
Experience in cross-functional environments delivering business-impacting data products and supporting on-call rotations.
Demonstrable projects using Spark, Kafka, cloud data platforms, and orchestration frameworks with measurable outcomes (reduced latency, cost savings, improved data quality).