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Databricks Engineering Consultant
Deloitte US
California, USA
Full-time
Entry · 2+ years experience
Onsite
USD 97700-162800 yearly / year
Discovered Yesterday
DatabricksPalantir FoundryPySparkSQLPythonApache Spark
Free
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DatabricksPalantir FoundryPySpark
Role Overview
Our Deloitte AI & Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients’ success. You’ll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
Key Skills for This Role
DatabricksPalantir FoundryPySparkSQLPythonApache Spark
Full Job Posting
Position Summary
Our Deloitte AI & Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients’ success. You’ll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
Work you'll do
The Audit AI & Automation Associate will support the assessment and mapping of the data, systems, and interface environment underpinning CIP, working across Databricks and/or Palantir Foundry ecosystems. This role acts as a junior architect, building a clear picture of how data moves, where it lives, and how systems connect — laying the groundwork for scalable, well-governed solutions. As a Data Engineer II on the AI & Data team, you will be responsible for:
Map and document existing data flows, pipelines, and integration points across Databricks and/or Palantir Foundry environments.
Assess current-state data architecture, including source systems, interfaces, and dependencies, to identify gaps, redundancies, and risks.
Catalog data sources, transformation logic, and downstream consumers to establish clear data lineage.
Evaluate existing ETL/ELT patterns (e.g., PySpark, SQL) for structure, quality, and alignment with governance standards, without owning day-to-day build work.
Partner with technical managers, analysts, and business stakeholders to gather requirements and translate them into architecture diagrams, system maps, and interface documentation.
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