Senior Databricks Data Engineer

Data Engineering & Pipeline Development:

Build, optimize, and maintain end-to-end data pipelines using Databricks, PySpark, and SQL

across ingestion, curation, and consumption layers.

Develop and manage Declarative Pipelines (e.g., Lakeflow / DLT-style pipelines) to support scalable

incremental processing and operational reliability.

Implement robust transformations and modelling patterns to deliver trusted datasets for

downstream consumption (analytics, operations, reporting, applications).

Data Quality, Controls & Operational Excellence:

Implement data quality validation, monitoring, reconciliation, and alerting to ensure datasets meet

required standards for completeness, accuracy, timeliness, and consistency.

Debug pipeline failures, resolve production incidents, and continuously improve pipeline stability,

performance, and cost efficiency.

Apply best practices around auditability, lineage, and data correctness — particularly in time-series

and historically tracked datasets.

Data Modelling & Domain Delivery

Contribute to the design and evolution of data models supporting the organization’s investment

footprint (Public Markets, Private Markets, reference/master data, corporate actions, portfolio,

pricing, risk, etc.).

Partner with business stakeholders to translate requirements into implementable data solutions

while preserving maintainability and governance standards.

Support integration of multi-vendor and internal data sources into curated datasets that align with

ADIA’s operational and analytical needs.

Platform & Engineering Standards

Follow and enhance engineering standards for version control, CI/CD, testing, documentation, and

secure development practices.

Optimize compute and storage usage through partitioning/clustering strategies, incremental

patterns, and performance tuning.

Contribute reusable libraries, patterns, templates, and approaches that improve delivery speed

and consistency across the team.

Mandatory Skills:

Bachelor’s degree (Computer Science, Engineering, Information Systems, or related discipline).

5+ years experience in data engineering roles (flexible based on depth of capability).

Strong hands-on experience with Databricks in production environments (prerequisite).

Strong programming experience with PySpark (must) and strong SQL (must).

Proven experience with Declarative Pipelines / pipeline orchestration on Databricks (prerequisite).

Strong understanding of data engineering fundamentals: ingestion patterns, transformation

design, incremental processing, testing, performance tuning.

Experience delivering production-ready datasets with appropriate operational controls

(monitoring, troubleshooting, reliability patterns).

Experience with modern Lakehouse concepts (Delta tables, optimization strategies, file skipping,

metadata/statistics awareness).

Exposure to data governance practices: cataloguing, documentation, business glossary/terms,

lineage.

Experience working in enterprise environments with CI/CD pipelines and structured release

processes.

Familiarity with vendor market data feeds (e.g., Bloomberg, Refinitiv, MSCI, FactSet) or similar

multi-source mastering patterns.

Strong Hands-on Expertise in Palantir Foundry. Proven experience with Foundry pipelines, ontologies, data lineage, transformations, and platform governance.

Proven Migration Experience from Palantir / to Databricks. Demonstrated experience leading or executing platform migrations, including pipeline conversion, data model redesign, and production cutover.

Familiarity with Dynatrace or Datadog for system observability and monitoring.

Databricks certification, cloud certifications (Azure/AWS), or enterprise data architecture certifications.