Secret Data Warehouse Modernization Developer

Insight Global

Fairfax, VA, United States

2 months ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

juju.com

Tech stack

Artificial Intelligence Amazon Web Services Business Logic Unit Testing Microsoft Azure Cloud Computing Data Infrastructure Extract Transform Load (ETL) Data Transformation Data Warehousing Dimensional Modeling Apache Hadoop

+18 more

Python (Programming Language) Microsoft SQL Server Cloud Services Reverse Engineering SQL Databases SQL Server Integration Services SQLAlchemy Web Application Frameworks Data Ingestion Snowflake Apache Spark Technical Debt Git SC Clearance Pandas Pyspark Azure Synapse Analytics Databricks

Job description

Insight Global is seeking a Senior Data Warehouse Modernization Developer to support a long term federal data modernization initiative. These engineers focus on modernizing data warehouse pipeline architecture by gradually reducing reliance on SSIS and SQL-only patterns, introducing Python based ETL, improving modularity, and setting the groundwork for a future move into PySpark, distributed compute, and eventually Databricks or similar cloud platforms. They should have an in depth understanding of the Kimball model in place, and experience incrementally replacing or re platforming components. Responsibilities include:

Learn and understand the existing Kimball-modeled SQL Server structure over time.
Reverse-engineer SSIS packages and convert them into Python-based modular ETL pipelines.
Build reusable Python frameworks for extraction, transformation, and loading.
Begin introducing PySpark for scalable transformation patterns.
Contribute to planning a future transition path (Databricks, Azure Synapse, Snowflake, etc.).
Improve orchestration, dependency management, and data quality practices.
Identify technical debt and modernize aging ETL components.
Collaborate with EDW SMEs to understand business logic and lineage.
Build documentation and repeatable processes for modernization.

Requirements

Active Secret clearance

Solid SQL and understanding of Kimball dimensional modeling.
Strong Python: Pandas, SQLAlchemy, packaging, module structure, unit testing.
Experience with PySpark or Spark (or willingness to ramp quickly).
Familiarity with cloud data platforms (Azure, AWS, etc.).
Experience modernizing legacy ETL systems is a major plus.
Ability to read, interpret, and redesign SSIS/SQL-based ETL logic.
Understanding of data ingestion, transformation, and structured warehouse design.
Experience with Git, CI/CD pipelines, and modern development practices. - Exposure to Databricks, Snowflake, or Hadoop ecosystems
Cloud experience (Azure or AWS)
Experience working in federal environments (DOJ, DEA, FBI, DHS, etc.)
Experience supporting both classified and unclassified systems
Experience with large scale or high volume data environments
Exposure to AI / data experimentation initiatives