Data Engineer

Design, build, and maintain data pipelines that ingest, transform, and deliver enterprise data to AI solutions and business applications.

Develop and manage integrations across enterprise data sources using APIs, Graph connectors, event-driven architectures, and batch/streaming patterns.

Build and maintain data stores and indexing infrastructure that support retrieval-augmented generation (RAG) and other AI consumption patterns.

Implement data quality, validation, and lineage controls to ensure accuracy and trustworthiness of data feeding AI workflows.

Support and optimize data models underpinning Power BI dashboards and AI-enabled analytics.

Collaborate with AI Engineers to define data contracts and ensure pipeline outputs meet solution requirements for schema, latency, and freshness.

Instrument data pipelines for monitoring, alerting, cost control, and performance optimization.

Implement data governance and security controls including access management, encryption, and compliance with organizational data policies.

Identify data-related risks and support mitigation strategies in collaboration with architecture and platform teams.

4+ years of data engineering experience, with demonstrated ability to build and operate production data pipelines.

Proficiency in Python and SQL; experience with PySpark or Spark is strongly preferred.

Experience with Azure data services, including Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, and Azure SQL.

Familiarity with Azure AI Search, Cosmos DB, or similar services used to support AI retrieval and storage patterns.

Experience building and managing ETL/ELT pipelines with structured, semi-structured, and unstructured data sources.

Knowledge of data modeling, schema design, and indexing strategies for both analytical and AI workloads.

Familiarity with infrastructure-as-code and CI/CD practices for data pipeline deployment (e.g., Terraform, Azure DevOps).