Hire Data Engineers

Why Hiring Data Engineers Is One of the Toughest Technical Searches

Data engineering sits at the intersection of software engineering, database administration, and distributed systems — making it one of the most technically demanding roles to hire for. Companies need data engineers who can build pipelines that reliably move terabytes of data across systems, transform it into usable formats, and ensure data quality at every step. Finding candidates with this combination of skills is exceptionally difficult.

The field is also evolving rapidly. The modern data stack has shifted from monolithic ETL tools to a landscape of specialized technologies — orchestrators like Airflow and Dagster, streaming platforms like Kafka, transformation tools like dbt, and a choice between data lakes, lakehouses, and traditional warehouses. A candidate who built pipelines with legacy tools five years ago may not be equipped for the architecture decisions your team faces today.

Exodata's engineers evaluate data engineering candidates on their ability to design end-to-end data architectures, not just use individual tools. We assess pipeline design thinking, data modeling decisions, failure handling strategies, and the ability to reason about data quality and consistency at scale. You get data engineers who can build the infrastructure that powers your analytics, machine learning, and business intelligence efforts.

What Our Engineers Assess

Every data engineer candidate goes through a live technical assessment with our engineering team. Here's what we evaluate:

• Pipeline architecture — batch vs. streaming design, orchestration patterns, idempotency, backfill strategies, and failure recovery
• Data modeling — dimensional modeling, slowly changing dimensions, star/snowflake schemas, and modeling for analytics vs. operational use cases
• Distributed processing — Spark optimization, partitioning strategies, shuffle management, and resource tuning for large-scale workloads
• Data quality and governance — validation frameworks, data lineage tracking, schema evolution, and anomaly detection in pipelines
• Cloud data platforms — designing on AWS (Glue, Redshift, S3), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow, Cloud Storage)
• SQL and transformation depth — complex query optimization, window functions, CTEs, incremental processing patterns, and dbt project structure

Common Tech Stacks We Vet For

Engagement Options