Data Bricks Engineer

Expert In Recruitment Solutions

🇺🇸 United States

On-site

5 months ago

Databricks
Azure
AI
MLflow
Unity Catalog
Delta Lake
Python
SQL
Azure Databricks
Data Modeling
RBAC
ABAC
CI/CD
Git
Scala
ELT
ETL
MLOps

5 months ago

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We are seeking a Databricks Engineer with Financial Services experience to design, build, and operate scalable, secure, and governed data and ML solutions on the Databricks platform on Microsoft Azure. In this role, you will partner closely with data engineering, CDAIO, architecture, data science, risk/compliance, and business stakeholders to deliver trusted Agentic AI solutions and production-grade AI workflows - leveraging Databricks MLflow, Lakehouse, Lakeflow, Feature Store, Unity Catalog, and Databricks Genie.
Required Qualifications

5+ years in data engineering and/or platform engineering with significant hands-on Databricks experience in production.
Demonstrated experience in Financial Services (banking, capital markets,ABS) with understanding of regulated data environments.
Strong hands-on expertise with one or more of the following:
- Databricks Lakehouse architecture and Delta Lake
- MLflow (tracking, registry, deployment workflow)
- Lakeflow (pipeline and workflow patterns)
- Databricks Feature Store
- Unity Catalog (governance, permissions, lineage, auditing)
- Databricks Genie (enablement aligned to governance)
Deep proficiency in Python and SQL; strong engineering practices (modular code, testing, code review, documentation).
Experience on Azure (core services relevant to data platforms), with real-world operation of Azure Databricks.
Practical expertise with data modeling, data quality management, and performance tuning for large-scale datasets.

Preferred Qualifications

Experience with streaming (e.g., structured streaming patterns) and event-driven architectures.
Knowledge of enterprise data governance and security controls (RBAC/ABAC patterns, encryption, key management, audit logging).
Experience implementing CI/CD for Databricks assets (e.g., Git-based workflows, automated deployment, environment promotion).
Familiarity with data and agent observability tooling and metrics (pipeline health, data freshness, schema drift, cost monitoring).
Prior experience building reusable feature repositories and standardized ML templates in regulated environments.

Technical Environment (Typical)

Platform: Azure Databricks
Core Capabilities: Lakehouse, Lakeflow, Delta Lake, Unity Catalog, MLflow, Feature Store, Genie
Languages: Python, SQL (Scala optional)
Patterns: Agentic Engineering, Medallion architecture, ELT/ETL, batch + streaming, data products, MLOps, governance-by-design

Data Bricks Engineer · Expert In Recruitment Solutions

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