Data Engineer II

Microsoft Microsoft · Big Tech · United States · Data Engineering

Data Engineer II role focused on building and maintaining ETL pipelines, data-sharing frameworks, and governance systems. Contributes to AI-ready pipelines and data products for intelligent systems, and designs backend services exposing datasets as data products. Also involved in reporting and analytics layer using Power BI. The role supports data ingestion, governance, exposure, and consumption for decision-making and AI-driven insights.

What you'd actually do

  1. Build and maintain production ETL pipelines ingesting data from 1st-party, 2nd-party, partner, and 3rd-party sources
  2. Design and implement data-sharing patterns and access controls (RBAC) across organizational boundaries
  3. Contribute to data mesh patterns, including data contracts, domain-owned datasets, and product-style publishing
  4. Develop and maintain data governance solutions, including lineage, cataloging, data quality, and compliance
  5. Design, build, and operate REST APIs and backend services that expose WISE datasets as data products

Skills

Required

  • Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 1+ year(s) experience in business analytics, data science, software development, data modeling, or data engineering OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 2+ years of experience in business analytics, data science, software development, data modeling, or data engineering OR equivalent experience

Nice to have

  • Experience designing and building production data solutions including ETL/data pipelines, backend services, or BI/reporting systems
  • Experience with PySpark, SQL, and Python, including data modeling, transformation, and pipeline development
  • Experience building or supporting REST APIs or data services, including schema design, versioning, and operations
  • Experience delivering Power BI or semantic modeling solutions, including DAX, Power Query (M), or curated datasets
  • Experience designing analytics or reporting with attention to performance, usability, and stakeholder consumption
  • Experience with data governance, data sharing, and access control (RBAC), including lineage, data quality, or compliance practices
  • Familiarity with data mesh, data products, or Data-as-a-Service concepts
  • Experience with Microsoft Azure or modern data platforms, including a combination of: Azure Data Factory, App Service, Functions, AKS, API Management, Microsoft Fabric (OneLake, Direct Lake), Synapse, Databricks, Azure SQL, or Cosmos DB
  • Experience with API standards and integration, including OpenAPI, Swagger, gRPC, GraphQL, or Microsoft Entra ID (Azure Active Directory)
  • Familiarity working in enterprise-scale data environments with diverse data sources
  • Experience contributing to data platforms, reporting hubs, or insights portals
  • Knowledge of privacy, security, or compliance practices, and interest in AI-ready data platforms (e.g., Copilot or agentic consumption patterns)

What the JD emphasized

  • AI-ready data platforms

Other signals

  • AI-ready pipelines
  • agentic or Copilot-based consumption
  • data products for intelligent systems