Design conceptual, logical, and physical data models for complex federal environments.
Lead the transition from legacy on-premises systems to modern, cloud-native (AWS/GCP) data platforms.
Architect and oversee the build of automated ETL/ELT pipelines using Python, SQL, and PySpark to ingest and transform unstructured and structured data.
Implement and optimize enterprise data warehouses using tools like AWS Redshift, Google BigQuery, AWS Glue, and Databricks.
Establish data governance frameworks, metadata management, and data lineage in alignment with federal standards (HIPAA, FHIR, NIST).
Conduct index/partition design, query tuning, and sharding strategies to ensure high availability and scalability for real-time analytics.
Design data architectures that facilitate AI/ML initiatives, including model training pipelines and real-time inference in production environments.
Mentor a team of data engineers, enforce software engineering best practices (CI/CD, unit testing, documentation), and serve as a technical bridge between stakeholders and delivery teams.

Requirements

**Experience:** 10+ years of progressive experience in data architecture, data modeling, or senior data engineering.
**Technical Stack:** * Expertise in **AWS** (S3, Redshift, Glue, Athena) or **GCP** (BigQuery, Cloud Functions).
Strong proficiency in **Python** (modular, testable code) and advanced **SQL**.
Experience with big data frameworks like **Spark**, **Dask**, or **Ray**.
**Education:** Bachelor’s degree in Computer Science, Information Technology, or a related field (Master’s preferred).
**Compliance:** Deep understanding of data security and federal compliance requirements.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data modelingETLELTPythonSQLPySparkAWS RedshiftGoogle BigQueryAWS GlueDatabricks

Soft Skills

mentoringleadershipcommunicationcollaborationproblem-solving