Azure Data Engineer

At CSpring, we believe in the power of data to drive decisions and real-world impact. We’re a purpose-driven consulting firm specializing in data strategy, data engineering, and data analytics. Our clients span the public and private sectors, and our work helps them solve complex problems, gain insights, and achieve measurable results.

We're seeking talented professionals who are collaborative, curious, and committed to making a difference that thrive at the intersection of data, technology, and business strategy. Whether you're passionate about transforming public programs, enabling executive decision-making, or modernizing legacy systems, you'll find meaningful work and purpose here.

Why You’ll Love Working Here

• Purposeful Projects – Improve systems that serve real people by delivering smarter data, streamlined processes, and strategic insight.
• People-First Culture – We’re as committed to your growth as we are to delivering high-impact solutions. You’ll find support, autonomy, and community here.
• Strategic, Hands-On Work – From data architecture and documentation to client workshops and solution delivery, you’ll influence every step of the process.
• Collaborative Trust – Our clients rely on us to listen carefully, deliver consistently, and guide wisely. We partner with integrity, curiosity, and heart.

What You’ll Do

You'll design, build, and support cloud data solutions on the Microsoft Azure stack for client engagements across government and healthcare.

• Build and orchestrate data pipelines in Azure Data Factory, moving data from source systems into curated, analytics-ready layers.
• Develop transformation logic in SQL, Python, or PySpark, and tune it so it holds up as data volumes grow.
• Design and maintain data models — dimensional, relational, or lakehouse — that make sense to the analysts and reporting teams who depend on them.
• Work in Azure Synapse, Databricks, Azure SQL, and Azure Data Lake Storage Gen2 to build storage and compute patterns that fit the client's scale and budget.
• Implement data quality checks, validation rules, and reconciliation so problems surface before a stakeholder finds them.
• Build monitoring, alerting, and error handling into pipelines from the start, and respond when something breaks in production.
• Manage code and deployments through Git and Azure DevOps, including CI/CD pipelines across development, test, and production environments.
• Partner with client analysts, program staff, and technical leads to translate business questions into data requirements you can build against.
• Document architecture, data lineage, and operational runbooks so the work is supportable after you hand it off.
• Follow client security, privacy, and compliance requirements in how you access, move, and store sensitive data.