Talent — Data Engineers & Analysts

Hire data engineers who build pipelines that don't break at 3 AM.

Zenaide places pre-vetted data engineers, analytics engineers, and data architects — from pipeline builders to warehouse designers — on a contract, contract-to-hire, or permanent basis.

Every company is a data company. Very few have the engineering team to prove it.

The gap between "we want to be data-driven" and "our data infrastructure actually works" is almost always an engineering problem. Dashboards don't build themselves. Pipelines don't stay reliable without engineers who design for failure, monitor for drift, and optimize for cost at scale.

Experienced data engineers who can design production-grade pipelines, model data for analytics and ML, and operate warehouse infrastructure reliably are among the hardest technical hires to make — because every company needs them and few can evaluate them properly.

Zenaide maintains an active pipeline of data engineers and analytics engineers who have built real data platforms — not just Jupyter notebooks.

Tools and platforms.

Apache SparkApache KafkaApache AirflowdbtSnowflakeDatabricksBigQueryRedshiftFivetranPythonSQLPandasDelta LakeAWS GlueAzure Data FactoryLooker / Tableau / Power BI

Capabilities.

Data pipeline architecture and orchestration
Data warehouse design and optimization
ETL/ELT pipeline development and monitoring
Real-time streaming and event-driven data systems
Data quality frameworks and observability
Data modeling (dimensional, Data Vault, wide-table)
Analytics engineering and semantic layer design
Data governance, cataloging, and lineage

Roles we place.

Data Engineers (Mid, Senior, Staff)
Analytics Engineers
BI / Reporting Engineers
Data Platform Engineers
Streaming / Real-Time Data Engineers
Data Architects

How we vet data engineering talent.

1

Pipeline design assessment

Evaluate ability to design scalable data pipelines — ingestion patterns, transformation logic, orchestration strategy, and failure handling.

2

SQL and modeling depth

Deep SQL assessment plus data modeling evaluation — dimensional modeling, slowly changing dimensions, incremental processing, and query optimization.

3

Tool and platform proficiency

Stack-specific evaluation across warehouse platforms (Snowflake, BigQuery, Redshift), orchestration tools (Airflow, dbt), and streaming systems (Kafka, Spark Streaming).

4

Production and collaboration skills

Data engineers serve every team. We assess ability to work with analysts, ML engineers, and business stakeholders — translating requirements into reliable data products.

Your next data hire is in our pipeline.

Tell us the stack, the data volumes, and the timeline. We'll present pre-vetted data engineers who build infrastructure that scales — and stays reliable.

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