Principal Data Engineer

Data Architecture & Engineering

• Architect, develop, and optimize complex data pipelines, data models, and ELT/ETL workflows across Synapse, Databricks, SQL Server, and cloud-native services.

• Lead large-scale data integration and modernization projects, expanding on the team’s responsibilities for analyzing requirements, creating specifications, and managing delivery schedules.

• Define and enforce best practices for data engineering, performance optimization, data quality, reliability, and observability.

Cross-Functional Collaboration

• Advise senior leaders and product owners on data strategy, platform capabilities, and architectural tradeoffs.

• Partner with Security, Infrastructure, and business teams to enable high‑quality, trusted, and governed data products.

Advanced Analytics & BI Enablement

• Design and optimize highly complex, high‑volume, and low‑latency data pipelines supporting analytics, operational workloads, and ML/AI use cases.

• Oversee implementation of robust data quality, lineage, cataloging, and metadata capabilities in partnership with Data Governance.

Cloud, Platform & Tooling Expertise

• Serve as the technical authority on Azure Synapse, Databricks, Spark, SQL Server, and cloud-native data services.

• Lead platform enhancements for performance, cost optimization, reliability, and global scale.

Mentorship & Technical Stewardship

• Coach and mentor Senior and Mid‑Level Data Engineers, elevating engineering capability across the organization.
• Lead technical design reviews, provide architectural guidance, and cultivate a culture of engineering excellence.
• Champion innovation through research, POCs, and evaluation of emerging technologies.

Who you are?

• 10+ years of data engineering, data architecture, or analytics engineering experience.
• Proven experience as a lead or principal-level engineer designing large-scale, distributed data systems.
• Expert-level proficiency in:
  • SQL, performance tuning, query optimization
  • Azure Synapse, Databricks, Spark/Delta Lake
  • ETL/ELT pipelines, orchestration frameworks
  • Data modeling, OLTP/OLAP, dimensional and semantic modeling
    • Cloud architecture patterns and distributed computing