SQL Data Engineer

Parra Consulting Group (PCG) is seeking a SQL Data Engineer to support federal health programs, regulatory science, and other high-impact missions. This position designs, develops, and optimizes scalable data solutions using advanced SQL and Databricks to deliver reliable insights that support mission-critical decisions.

This successful candidate will be responsible for the following:

Design, develop, and optimize scalable data pipelines, data models, and analytics-ready datasets using advanced SQL and Databricks.

Develop, tune, and troubleshoot complex SQL solutions, including queries, stored procedures, views, and functions, across Oracle SQL, PostgreSQL, and Databricks SQL.

Integrate data from databases, cloud data platforms, APIs, JSON, XML, and flat files.

Implement automated testing, data validation, data reconciliation, and data-quality processes.

Translate solutions across different SQL dialects and database environments.

Collaborate with data engineers, analysts, software developers, data scientists, and client stakeholders to deliver secure, high-performing solutions.

Communicate technical concepts to technical and non-technical audiences, and raise questions, risks, and recommended actions to management early.

Required Qualifications

Bachelor’s degree in Computer Science, Data Science, Engineering, Statistics, Biostatistics, or Mathematics.

5+ years of experience developing, optimizing, and troubleshooting complex SQL solutions.

Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements.

S. citizenship required to support this federal contract.

Required Experience

Experience developing, optimizing, and troubleshooting complex SQL solutions, including queries, stored procedures, views, functions, and data transformations, in Oracle SQL, PostgreSQL, and Databricks SQL.

2+ years of experience with Databricks, including Databricks Workspace, notebooks, jobs, workflows, Apache Spark, Delta Lake, and cluster configuration.

Experience with relational databases, data modeling, dimensional modeling, data warehousing, and designing and implementing medallion architecture patterns.

Experience integrating data from databases, cloud data platforms, APIs, JSON, XML, and flat files.

Experience developing data engineering solutions in Python and managing code through Git or comparable source-control tools.

Experience implementing automated testing, data validation, data reconciliation, and data-quality processes.

Ability to work independently in an Agile, collaborative, and client-focused environment and troubleshoot complex issues.

Preferred Qualifications

Experience with advanced Databricks capabilities, including Delta Live Tables, Workflows, and Unity Catalog.

Experience developing and optimizing ETL and ELT transformation workflows supporting reporting, analytics, or operational systems.

Experience with Pentaho or equivalent data integration and ETL tools.

Experience implementing CI/CD pipelines, automated deployments, and software development practices for data solutions.

Experience applying AI-enabled tools or techniques to improve data engineering workflows, pipeline performance, or developer productivity.

Experience with data governance, metadata management, data lineage, privacy, security, and access controls.

Experience supporting federal health data systems, regulatory science, or other regulated data environments.

Databricks, AWS Cloud, or data engineering technical certification.