Source-listed Job

Principal Data Engineer

Lead data-platform architecture: Design and evolve ETL workflows, data pipelines, and data models using Spark, Python, and SQL. Drive hands-on, end-to-end implementation: Build data solutions using Spark (or similar distributed sy...

Job Source description available
Microsoft Redmond, WA,US, US Source published Sep 30, 2026 Source retrieved Oct 11, 2026
Source: Microsoft Careers · A retrieval date records when our system last obtained the source record. It does not guarantee the vacancy is still open or that every detail has been independently checked.
Description from the source The source description is formatted below for discovery. The provider owns the original wording and may change its requirements or close applications.
Principal Data Engineer opportunity at Microsoft
DeadlineMon Mar 29 6:46 PM 2027
EmploymentF U L L T I M E
CountryUS

Overview

Lead data-platform architecture: Design and evolve ETL workflows, data pipelines, and data models using Spark, Python, and SQL. Drive hands-on, end-to-end implementation: Build data solutions using Spark (or similar distributed systems), open table formats such as Delta Lake, and Parquet files; develop scalable, secure APIs and data access controls, including RBAC; provision resources through Infrastructure as Code; and deliver production-grade deployments through CI/CD with observability and operational runbooks. Think beyond immediate requirements: Own complex system-design decisions, evaluate alternatives, and account for cross-team dependencies, scalability, resiliency, disaster recovery, and cost. Make evidence-based decisions: Proactively gather facts, validate assumptions, test hypotheses, and use telemetry and investigation to resolve complex problems rather than relying on unver

Full job description

Full Job Description

Lead data-platform architecture: Design and evolve ETL workflows, data pipelines, and data models using Spark, Python, and SQL. Drive hands-on, end-to-end implementation: Build data solutions using Spark (or similar distributed systems), open table formats such as Delta Lake, and Parquet files; develop scalable, secure APIs and data access controls, including RBAC; provision resources through Infrastructure as Code; and deliver production-grade deployments through CI/CD with observability and operational runbooks. Think beyond immediate requirements: Own complex system-design decisions, evaluate alternatives, and account for cross-team dependencies, scalability, resiliency, disaster recovery, and cost. Make evidence-based decisions: Proactively gather facts, validate assumptions, test hypotheses, and use telemetry and investigation to resolve complex problems rather than relying on unverified assumptions. Advance AI-native engineering: Apply AI across design, coding, testing, and troubleshooting; validate AI-generated outputs and help the team adopt effective, responsible practices. Own production outcomes: Establish monitoring and operational readiness, drive incident resolution and root-cause analysis, and implement preventive improvements. Automate safe deployments, targeting zero-touch deployment where possible. Raise engineering quality: Lead design and code reviews, strengthen testing, and embed security, privacy, and compliance into solutions throughout their lifecycle. Collaborate and mentor: Communicate trade-offs clearly, align partner teams on dependencies and ownership, mentor engineers, and share knowledge to improve team capabilities. Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 15+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience building data platforms for experimentation, metrics, and scorecards, including understanding success metrics, guardrail metrics, and data quality. Experience in Databricks like platforms.

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