Senior Investment Data Engineer (Databricks Lakehouse)

Tanqeeb; Abu Dhabi; On-site; AED 391,000 - 614,000; Full time

Job summary

Tanqeeb seeks an experienced Data Engineer to design, build, and operate end-to-end data pipelines and models powering analytics across investment teams and back-office functions.

You will ingest vendor and internal sources, model in a lakehouse, and ensure data is discoverable, reliable, and cost-efficient, partnering with BAs/PMs and quants to shape pragmatic architectures balancing speed with governance and scale.

Qualifications

• Bachelor's degree in CS/Engineering/IS or related field.
• 5+ years of data engineering experience.
• Strong hands-on Databricks in production environments.
• Strong PySpark and SQL proficiency.
• Experience with Declarative Pipelines / Databricks orchestration.
• Understanding of data engineering fundamentals: ingestion, transformation, incremental processing, testing, tuning.
• Experience delivering production datasets with monitoring and reliability patterns.
• Familiarity with vendor market data feeds or multi-source mastering patterns.

Responsibilities

• Participate in requirements clarification and sprint planning sessions.
• Design and implement ETL pipelines in PySpark; build robust data pipelines.
• Optimize ETL processes for performance, scalability, and reliability.
• Write unit and integration tests.
• Support QA teammates in the acceptance process.
• Resolve PROD incidents as a 3rd line engineer.

Skills

PySpark

SQL

Databricks

ETL pipelines

Pipeline orchestration

Data governance

Testing

CI/CD pipelines

Foundry pipelines

Data modeling

Education

Bachelor's degree in Computer Science/Engineering/IS

Tools

Databricks

Palantir Foundry

Foundry pipelines

Dynatrace

Datadog

Job description

Tanqeeb seeks an experienced Data Engineer to design, build, and operate end-to-end data pipelines and models powering analytics across investment teams and back-office functions.

You will ingest vendor and internal sources, model in a lakehouse, and ensure data is discoverable, reliable, and cost-efficient, partnering with BAs/PMs and quants to shape pragmatic architectures balancing speed with governance and scale.