Software Engineering
Big Data Developer Job Description
Big Data Developers design and build systems that process, store, and analyze datasets too large for traditional databases — building distributed data pipelines using Spark, Kafka, and cloud data platforms, implementing batch and streaming data workflows, and delivering the reliable data infrastructure that analytics, machine learning, and reporting systems depend on.
Last updated
Role at a glance
- Typical education
- Bachelor's or Master's degree in CS, Data Science, or a quantitative field
- Typical experience
- 3-7 years
- Key certifications
- Databricks Certified Data Engineer, GCP Professional Data Engineer, AWS Data Engineer Associate
- Top employer types
- Financial services, tech, healthcare, logistics
- Growth outlook
- High-growth driven by the continued expansion of data generation across all industries
- AI impact (through 2030)
- Accelerating demand as AI/ML systems require scalable infrastructure for feature stores, distributed training, and LLM training pipelines.
Duties and responsibilities
- Design and implement distributed data processing pipelines using Apache Spark for batch and streaming workloads
- Build and maintain real-time data streaming systems using Apache Kafka, Kafka Streams, or Apache Flink
- Develop ETL and ELT workflows that ingest data from diverse sources into data lakes and data warehouses
- Optimize Spark job performance: partitioning strategies, broadcast joins, caching, and cluster resource tuning
- Design data lake architectures on S3, GCS, or Azure Data Lake with appropriate file formats (Parquet, Delta Lake, Iceberg)
- Write and maintain data transformation pipelines in PySpark, Scala, or SQL for analytics and ML feature engineering
- Implement data quality checks, schema validation, and lineage tracking across pipeline stages
- Monitor pipeline health, diagnose failures, and build alerting for SLA breaches on data freshness and completeness
- Collaborate with data analysts and ML engineers to understand data requirements and design schemas that support their workloads
- Manage and optimize cloud data warehouse costs in Snowflake, BigQuery, Redshift, or Databricks SQL
Overview
A Big Data Developer builds the systems that process, transform, and store data at scales beyond what conventional tools handle — billions of events, terabytes of log data, continuous streams of transactions arriving every second. Their infrastructure is what makes analytics, machine learning, and reporting possible on data that would overwhelm a traditional database.
The core work involves distributed data pipelines. A batch pipeline might ingest a day's worth of transaction data from S3, join it with customer reference data from a database, apply business transformations, and write the result to a data warehouse where analysts can query it. A streaming pipeline might consume an event stream from Kafka, apply windowed aggregations in real time, and write aggregated results to a low-latency serving database. Both require distributed computation because the data is too large for one machine — which means understanding how Spark or Flink distribute work across a cluster, how to partition data to avoid hotspots, and how to tune cluster resources to balance cost and performance.
Data quality and reliability are often more challenging than the distributed processing itself. Real data is messy: records arrive late, schemas change without warning, upstream systems have bugs that produce incorrect values, and pipelines fail partway through leaving partial results. Big Data Developers build validation checks at every stage, implement idempotent writes that can be retried without duplicating data, and design pipelines that can backfill historical data when issues are discovered after the fact.
Cloud data platform management is a significant part of the role. Databricks, Snowflake, BigQuery, and Redshift each have their own performance tuning characteristics, cost models, and operational tooling. Developers who understand how to size clusters, choose between auto-scaling and fixed clusters for different workload profiles, and write SQL that the query optimizer can execute efficiently are consistently more valuable than those who just write Spark jobs without thinking about the economics.
Collaboration is constant. Analysts report that queries are slow. ML engineers need feature tables with specific freshness requirements. Data product managers ask when the pipeline will be ready for a new data source. Big Data Developers work across these stakeholders, translating technical constraints into expectations and data requirements into pipeline designs.
Qualifications
Education:
- Bachelor's or Master's degree in computer science, data science, software engineering, or a quantitative field
- Strong self-taught candidates with demonstrable Spark and data pipeline projects are competitive
- Graduate degrees are more common in this specialization than in general software engineering, particularly at research-adjacent companies
Experience:
- 3–7 years of data engineering or big data development experience
- Production Apache Spark experience — not just tutorials, but tuned production jobs at meaningful scale
- Experience with at least one streaming framework (Kafka, Flink, Spark Streaming)
Core technical skills:
- Apache Spark: PySpark (primary) and Scala Spark; DataFrames API, Spark SQL, Structured Streaming, RDD layer for optimization work
- Apache Kafka: producer and consumer client programming, Kafka Streams, partition design, consumer group management
- Data lake formats: Delta Lake (Databricks), Apache Iceberg (AWS, Snowflake), Apache Hudi
- SQL: advanced analytical SQL — window functions, CTEs, lateral views, query optimization
- Python: the dominant language for data engineering; pandas for exploration, PySpark for scale
Cloud data platforms:
- Databricks: workspace administration, cluster configuration, Delta Live Tables, Unity Catalog
- Snowflake: warehouse sizing, clustering keys, data sharing, query optimization
- BigQuery: partitioning and clustering, slot reservations, cost optimization
- AWS: EMR, Glue, Redshift, Lake Formation, Kinesis
- GCP: Dataproc, Dataflow, Cloud Composer (managed Airflow)
Orchestration:
- Apache Airflow: DAG authoring, operator selection, XCom patterns, backfill operations
- Prefect, Dagster, or Mage as alternatives in modern data stacks
Infrastructure:
- Terraform or CloudFormation for data infrastructure provisioning
- Docker and Kubernetes for containerized Spark and pipeline workloads
- dbt for transformation layer in modern data stack architectures
Career outlook
Big Data development is a high-growth, well-compensated specialization driven by the continued expansion of data generation across all industries. The volume of data created by IoT devices, user activity tracking, transaction systems, and log aggregation continues to grow, and the analytics and ML systems that consume that data require reliable, scalable infrastructure to function.
Databricks and the modern data stack have significantly simplified the tooling landscape compared to the Hadoop-era infrastructure of 10 years ago. Managed Spark clusters, Delta Lake, and cloud-native data warehouses have reduced the infrastructure management burden, allowing Big Data Developers to spend more time on pipeline logic and data quality than cluster administration. This has made the skill set more accessible while also raising the bar for what's expected.
The integration of AI and ML into big data workloads is the defining trend of 2026. Every significant ML system depends on data infrastructure to create features, run training jobs at scale, and serve predictions — and the people who build that infrastructure are Big Data Developers. Feature store implementation, distributed training data preparation, and LLM training data pipelines are all growth areas within big data development.
The certification landscape is maturing and credentials are increasingly recognized by employers. Databricks Certified Data Engineer Associate and Professional certifications command measurable salary premiums. GCP Professional Data Engineer and AWS Data Engineer Associate are valued at cloud-standardized organizations. These certifications provide structured learning paths and signal genuine platform competency to employers.
Senior Big Data Developers ($145K–$175K) and principal/staff data engineers ($175K–$220K at major tech and financial services firms) represent the upper compensation bands. The field continues to grow faster than the developer pipeline, keeping compensation strong. Financial services, tech, healthcare, and logistics are the four largest employment sectors for this specialization.
Sample cover letter
Dear Hiring Manager,
I'm applying for the Big Data Developer position at [Company]. I've been a data engineer at [Company] for four years, building and maintaining the Spark-based data pipelines that feed our analytics warehouse and machine learning feature store.
The system I'm most technically invested in is a real-time event processing pipeline that consumes click and session data from our Kafka cluster — about 80,000 events per second at peak — aggregates user behavior features using Spark Structured Streaming, and writes them to a Delta Lake table that our ML team's models read from. The technical challenge was getting the watermarking and window size tuned correctly to balance latency (ML team wanted feature freshness within 5 minutes) against completeness (late-arriving events from mobile clients with spotty connectivity could arrive 15 minutes late).
I've also done significant Databricks cost optimization work. When I joined, we were running an always-on autoscaling cluster that cost $28K per month regardless of actual workload. I migrated the batch jobs to job clusters that spin up only when running, implemented cluster pooling for short-running jobs to reduce spin-up time, and used Photon for the heaviest SQL transformation jobs. Monthly compute cost dropped to $11K while pipeline throughput stayed the same.
I hold the Databricks Certified Data Engineer Professional certification and I'm working toward the Certified ML Engineer Associate. I'd welcome the opportunity to discuss the role.
[Your Name]
Frequently asked questions
- What does a Big Data Developer do?
- Big Data Developers design and build systems that process, store, and analyze datasets too large for traditional databases — building distributed data pipelines using Spark, Kafka, and cloud data platforms, implementing batch and streaming data workflows, and delivering the reliable data infrastructure that analytics, machine learning, and reporting systems depend on.
- What are the main duties of a Big Data Developer?
- Core duties include: design and implement distributed data processing pipelines using Apache Spark for batch and streaming workloads; build and maintain real-time data streaming systems using Apache Kafka, Kafka Streams, or Apache Flink; and develop ETL and ELT workflows that ingest data from diverse sources into data lakes and data warehouses.
- What is the difference between a Big Data Developer and a Data Engineer?
- The terms overlap significantly. 'Data Engineer' is the broader and more current title — it covers building data pipelines, data warehouses, and data integration systems at any scale. 'Big Data Developer' specifically emphasizes the distributed processing context — Spark, Hadoop, Kafka — and implies working with data volumes too large for single-machine tools. Most Big Data Developer job descriptions are effectively senior or specialized data engineering roles.
- Is Hadoop still relevant for Big Data Developers?
- Hadoop HDFS as a storage layer has largely been supplanted by cloud object storage (S3, GCS, Azure Data Lake Storage) for new deployments. However, Hadoop ecosystem components like YARN, Hive, and HBase still run in many enterprise environments and remain relevant for maintenance and migration work. Spark runs on Hadoop clusters, on cloud-managed services (EMR, Dataproc), and increasingly on Kubernetes — so understanding Spark doesn't require understanding Hadoop, but knowledge of both matters at companies with existing Hadoop infrastructure.
- What is Delta Lake and why is it important?
- Delta Lake is an open-source storage layer that adds ACID transactions, schema enforcement, and time travel (queryable historical versions) to data lake storage. It solves the data reliability problems that made raw data lakes difficult to query accurately — concurrent writes creating inconsistent reads, schema drift breaking downstream pipelines, and inability to roll back bad data loads. Databricks built Delta Lake, but the format is open and supported across the major cloud platforms. It's rapidly becoming the standard for production data lakes.
- How do Big Data Developers handle late-arriving data in streaming pipelines?
- Late-arriving data — events that arrive after the time window they belong to has already been processed — is a fundamental challenge in streaming systems. Approaches include watermarks (accepting late data up to a defined threshold), event time vs. processing time distinctions (using when an event happened rather than when it was received), and reprocessing triggers that can update results when late data arrives. Apache Flink and Spark Structured Streaming both have built-in support for these patterns, but the configuration requires understanding the tradeoffs between latency, completeness, and computational overhead.
- How is AI changing the Big Data Developer role?
- Machine learning and AI workloads increasingly depend on the same data infrastructure that Big Data Developers build. Feature stores — centralized repositories of ML features computed from raw data — are now a standard component in production ML systems, and Big Data Developers often own their implementation. The rise of LLM training and fine-tuning has also created demand for developers who can build distributed data pipelines for text data at scale. Big Data developers who develop ML infrastructure skills are moving into MLOps and AI platform roles.
Related job descriptions
See all Software Engineering jobs →- Big Data Engineer$127K–$181K
Big Data Engineers design and build the infrastructure and pipelines that collect, store, process, and serve large-scale data sets. They work with distributed computing frameworks, cloud data warehouses, and streaming platforms to move data from source systems to the analytics and ML environments where it becomes useful: reliably, at scale, and with quality that downstream consumers can trust. In 2026 the role is shifting further toward real-time serving on lakehouse tables (Delta Lake, Apache Iceberg) rather than overnight batch jobs alone.
- Data Warehouse Developer$85K–$135K
Data Warehouse Developers design, build, and maintain the data storage systems that power business intelligence and analytics. They write ETL pipelines, model dimensional schemas, and optimize query performance so analysts and executives can pull accurate, fast reports from large volumes of operational data. Most work closely with data engineers, BI developers, and database administrators in corporate IT or analytics teams.
- Data Scientist$100K–$160K
Data Scientists analyze large datasets, build predictive models, and communicate insights that drive business decisions. They combine statistical methods, machine learning, and programming to identify patterns, test hypotheses, and build systems that generate value from data. The role spans exploratory analysis, model development, and working with engineers to deploy models into production.
- AI Data Curator$72K–$130K
AI Data Curators source, clean, label, and maintain the datasets that machine learning models train on. They sit at the intersection of data engineering and research operations — ensuring that the inputs feeding a model are accurate, representative, consistently formatted, and free from the quality problems that silently corrupt model behavior. This role is foundational to any serious ML pipeline and has grown substantially as the scale of training data requirements has increased.
- AI Data Engineer$105K–$175K
AI Data Engineers design, build, and maintain the data infrastructure that powers machine learning systems — pipelines, feature stores, data lakes, and real-time streaming architectures that feed model training and inference at scale. They sit at the intersection of data engineering and MLOps, translating raw, messy data sources into clean, versioned, and observable datasets that data scientists and ML engineers can actually use in production.
- AI Data Quality Engineer$95K–$160K
AI Data Quality Engineers design, implement, and maintain the validation frameworks, pipelines, and monitoring systems that ensure training data, inference inputs, and ground-truth labels meet the standards ML models require to perform reliably. They sit at the intersection of data engineering and ML operations, owning the processes that catch label errors, schema drift, distribution shift, and upstream data corruption before those problems propagate into model behavior or production predictions.