What You'll Do

Design and implement high-throughput, event-driven architectures to power near real-time data pipelines for ingesting into Azure + Databricks Lakehouse

Build cloud-native data pipelines to support batch ingest workloads

Configure Unity Catalog by designing Bronze/Silver/Gold medallion architecture and optimize for centralized data governance, fine-grained access control, and end-to-end lineage tracking across Databricks workspaces

Drive engineering excellence by setting standardized DevOps practices and optimizing data engineering code for performance

Identify and design solutions to potential data processes that can be improved with automation

Partner with domain teams to onboard new capability use cases, mentor engineers, and contribute to reusable blueprints and reference implementations.

What You'll Bring

5+ years of software engineering experience focused on data infrastructure and backend systems.

Hands-on experience with the Microsoft Azure data platform - Azure Data Factory, Event Hubs, Blob Storage, ADLS Gen2, and Azure DevOps for orchestration and CI/CD

Production experience with Azure Databricks and Delta Lake

Production experience with PySpark for distributed data processing and transformation.

Streaming frameworks (Kafka or comparable) for real-time data paths including CDC and incremental batch patterns.

Strong communication skills with the ability to influence senior stakeholders and lead technical decisions across global teams.

Must Have Skills

Data Platform Architecture (Lakehouse, Delta Lake, Azure Databricks)

Kafka / Streaming Frameworks

Strong SQL and Python Proficiency

Experience with Cloud Native Tools

Nice-to-Have

Building data infrastructure for AI use cases

Prior experience in large-enterprise data integration or platform engineering environments

Prior experience in enterprise platform migrations