Big Data Engineer Job Description

Big Data Engineers build the systems that make large-scale data usable. That sounds straightforward, but the actual work spans distributed computing, cloud infrastructure, data modeling, quality management, and the people work of understanding what analysts, data scientists, and business users actually need from the data they're building pipelines to deliver.

A typical data engineer's work divides across several concerns. Pipeline development is the most visible: designing and implementing the jobs that read from source systems, databases, event streams, third-party APIs, log files, transform the data into a useful shape, and load it into the storage layer where it will be queried. At any interesting scale this means distributed processing: Spark for batch, Kafka and Flink for streaming, and orchestration tools like Airflow to schedule and monitor it all.

Storage architecture is equally important, and it's the part of the job that changed most in 2026. Data lakes built on object storage can become expensive and unusable if not designed carefully: wrong file formats, missing partitioning, inconsistent naming conventions, and inadequate access controls compound over time into systems that cost too much and produce results no one trusts. Table formats like Delta Lake and Apache Iceberg have become the default answer to that problem, adding ACID transactions, schema enforcement, and time travel on top of plain object storage. Databricks' June 2026 announcement of Lakehouse//RT, a real-time compute engine running directly on governed Delta Lake and Iceberg tables, signals that the industry is pushing lakehouse architecture beyond batch analytics into millisecond-latency serving, which changes how engineers design storage layouts from the start.

Data quality is a persistent challenge. Source systems produce corrupt records, schema changes break pipelines unexpectedly, and the data users rely on for decisions can drift from reality without anyone noticing until something is wrong. Building quality checks into the pipeline, not just at the end but at each transformation stage, is work that most data engineers wish they had done earlier in their platform's life. That work has only grown more important as AI tools make it faster to generate pipeline code, which means more pipelines shipping without a matching increase in the governance work needed to trust their output.

The role increasingly involves collaboration with the people consuming data. Analysts who write inefficient queries, data scientists who don't understand partitioning, and business users who don't know the limitations of the data they're using all create costs that flow back to the data engineering team. Engineers who understand the downstream use cases build better platforms, and Robert Half's description of the role as a liaison between business stakeholders and data scientists reflects how much of the job is translation work as much as engineering.

Education:

• Bachelor's degree in computer science, software engineering, or a quantitative discipline
• Data engineering is a field where demonstrated skills, a GitHub portfolio, public datasets, certifications, matter more than credentials from a specific school

Experience:

• Robert Half's 2026 guide lists 4-6 years of experience as typical for the role; senior architecture-track positions generally ask for more
• Production experience with at least one major distributed processing framework and one cloud data platform
• Demonstrated experience building pipelines that run reliably in production, not just in development

Core technical skills:

• Python and Java are the two languages Robert Half calls out as baseline expectations, alongside pandas, PySpark, and data quality libraries such as Great Expectations or Soda
• Distributed processing: Apache Spark (PySpark or Scala), Apache Flink for streaming
• Orchestration: Apache Airflow, Prefect, Dagster, covering DAG design, failure handling, and SLA monitoring
• SQL: advanced window functions, query optimization, partitioned table design
• Streaming: Apache Kafka, covering producer/consumer patterns, topic design, and consumer group management
• NoSQL: familiarity with document, key-value, or wide-column stores is explicitly called out by employers alongside relational SQL skills

Cloud data platforms:

• AWS: EMR, Glue, Athena, Kinesis, S3, Redshift
• GCP: Dataproc, Dataflow, BigQuery, Pub/Sub, GCS
• Azure: Synapse Analytics, Data Factory, Event Hubs, ADLS
• Databricks and Snowflake, cross-cloud platforms used across all three

Data engineering practice:

• dbt for SQL transformation modeling in cloud warehouses
• Delta Lake or Apache Iceberg for ACID transactions on lakehouse storage, now the standard rather than the exception on new builds
• Data catalog tools: Apache Atlas, Alation, DataHub for lineage and metadata
• Infrastructure: Terraform or CloudFormation for provisioning data infrastructure reproducibly

What hiring managers weigh most:

• Evidence the candidate has operated pipelines under real production load, not just built demos: on-call rotations, incident postmortems, and SLA commitments carry more weight than a long tool list
• Comfort reading and writing infrastructure-as-code, since data platforms increasingly live alongside application infrastructure in the same Terraform state
• The ability to explain a past architecture decision and its tradeoffs clearly to a non-technical stakeholder, which mirrors the liaison function Robert Half's role description calls out explicitly
• Familiarity with at least one modern table format migration (moving raw Parquet or CSV into Delta Lake or Iceberg), since so many teams are mid-migration in 2026 and want engineers who have done it before

Data engineering remains one of the more resilient specializations in technology hiring. Robert Half's 2026 Technology Salary Guide shows the national salary range for Data Engineers running from $127,000 to $180,750, with a $156,250 midpoint, and calls out data engineering alongside cloud architecture and cybersecurity as a skill area where employers are paying above market to secure candidates. That's a meaningfully higher band than the broader Database Administrators and Architects occupation the BLS tracks, which had a $126,760 median wage in May 2025 and a job outlook of 4 percent (as fast as average) for 2025-35, a reminder that the BLS category is a floor for generalist database work, not a ceiling on specialized pipeline engineering pay.

The direction of the field in 2026 is toward real-time serving and away from batch-only design. Databricks' June 2026 launch of Lakehouse//RT, a compute engine delivering millisecond query latency directly on governed Delta Lake and Apache Iceberg tables, is one concrete signal that lakehouse architecture is being extended from analytics-at-rest into live serving for applications and AI agents. Engineers who can design streaming ingestion and low-latency table layouts, not just overnight batch jobs, are positioned ahead of that shift.

AI is reshaping the day-to-day differently than it reshapes the job market. dbt Labs' 2026 State of Analytics Engineering Report frames the central 2026 dynamic as AI scaling analytics and pipeline output faster than the governance and trust mechanisms designed to keep pace with it. In practice, that means AI-assisted code generation is speeding up how quickly engineers can write Spark jobs or dbt models, but it is simultaneously increasing the amount of data quality, lineage, and access-control work needed to keep faster-shipping pipelines trustworthy. Engineers who own that governance layer, rather than just the pipeline code, are the ones organizations are trying hardest to retain.

Career paths lead in several directions. Senior Data Engineers often move into Staff or Principal Engineer roles with cross-team architectural scope. Some shift into data architecture, data platform leadership, or engineering management. Others migrate toward ML engineering as the boundary between data engineering and ML infrastructure continues to blur, particularly around feature stores and real-time inference logging. Compensation at senior levels is competitive with software engineering and cloud architecture, and the Robert Half figures above sit near the top of what comparable engineering specializations command.

Geography still matters for pay, though less than it used to. Robert Half's figures are national averages; engineers in major tech hubs and financial-services centers typically land above the $180,750 high end, while smaller markets cluster closer to the $127,000 floor. Remote-first hiring has narrowed that gap somewhat since 2023, but companies running regionally-adjusted pay bands still price the role differently by metro area, so candidates should treat the Robert Half range as a national anchor rather than a promise for any one city.

Dear Hiring Manager,

I'm applying for the Big Data Engineer position at [Company]. I've spent the past four years building and maintaining data infrastructure at [Company], where the data platform I own processes around 2 TB of event data daily across batch and streaming pipelines.

The project I'm most proud of is a migration of our storage layer from an ad-hoc collection of Parquet files to a Delta Lake architecture with schema enforcement and time travel. The previous setup had accumulated three years of inconsistently partitioned files across roughly 40 naming conventions. The migration took five months but gave our analytics team the reliable, queryable foundation they'd been asking for since before I joined, and it positioned us to evaluate real-time query engines like the ones now emerging on top of Delta Lake and Iceberg.

I also led the rollout of Airflow-orchestrated PySpark jobs with Great Expectations checkpoints at each transformation stage and a Slack alerting system that pages on data freshness SLA breaches. Pipeline failures that previously went undetected for hours are now caught within ten minutes, which matters more every quarter as more of our AI features depend on that data being current.

I'm looking to move into a role with more streaming infrastructure work, specifically Kafka and Flink, and I've been following the shift toward real-time lakehouse serving that platforms like Databricks are pushing this year. The real-time pipeline requirements in your job description are exactly the direction I want to grow. I'd welcome the chance to talk about what you're building.

[Your Name]