This is a short-term, project-based engagement best suited to an experienced 1099 independent contractor who's comfortable moving fast in an ambiguous, fast-paced environment.
You'll be working within the client's data integration and warehousing stack, helping build and maintain pipelines that move and shape data for downstream reporting. You'll work closely with client-side stakeholders as well as the CompassX team, so clear communication and the ability to translate technical work into business context matters as much as the engineering itself. You'll collaborate closely with a small group of engineers and analysts, so the ability to work autonomously while staying tightly aligned with the team is essential.
What You'll Do
Design, build, and maintain scalable ETL/ELT pipelines using IICS to integrate data from source systems into the warehouse.
Integrate complex commercial datasets utilizing newly established API ingestion methods, alongside multiple manual data sources.
Develop and optimize data models and structures within Amazon Redshift.
Write production-quality SQL and Python to transform, validate, and automate data workflows.
Build and support Power BI data models that feed reporting and dashboards for business stakeholders.
Implement data quality checks and validation logic to ensure accuracy and traceability across pipelines.
What You'll Bring
Hands-on experience with IICS (Informatica Intelligent Cloud Services) for data integration.
Experience working with Amazon Redshift as a data warehouse platform.
Experience extracting and processing complex data sets via APIs.
Ability to onboard quickly and operate independently in a fast-paced, aggressive 12-week project timeline.
Strong SQL skills and solid grounding in ETL/ELT pipeline design.
Proficiency in Python for data engineering workflows.
Experience building Power BI data models, ideally including a strong grasp of data modeling best practices for reporting layers.
Comfort working with cloud-based data infrastructure (AWS preferred, given the Redshift environment).
An eye for data quality and validation — accuracy and traceability are critical for this environment.
Bonus: familiarity with commercial pharma CRM and operations data sets.
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