Azure Data Engineer

CSpring•Indianapolis, IN

About The Position

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.

Requirements

3+ years of hands-on data engineering experience, including 2+ years building on Azure.
Strong SQL — complex joins, window functions, stored procedures, and query performance tuning.
Practical experience with Azure Data Factory, including pipeline design, triggers, parameterization, and error handling.
Working experience with at least one of Azure Synapse Analytics, Databricks, or Azure SQL.
Python or PySpark for data transformation and automation.
Experience with data modeling and warehouse design concepts, including dimensional modeling.
Familiarity with Azure Data Lake Storage Gen2 and how to organize raw, curated, and consumption layers.
Version control and deployment discipline using Git and Azure DevOps.
Clear communication with non-technical stakeholders — you can explain what you built and why it matters.
Bachelor's degree or equivalent hands-on experience.

Nice To Haves

Prior consulting or client-facing delivery experience.
Power BI or supporting downstream reporting and analytics teams.
Microsoft Azure certification (DP-203 or equivalent).
Experience with data governance tooling such as Microsoft Purview.

Responsibilities

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.