Lead Data Engineer

About this role

Type of Requisition: Pipeline

Clearance Level Must Currently Possess: None

Clearance Level Must Be Able to Obtain: None

Public Trust/Other Required: None

Job Family: Data Science and Data Engineering

Job Qualifications: Skills: Data Analysis, Data Analytics, Data Lake, Data Warehousing (DW) Certifications: None Experience: 7 + years of related experience US Citizenship Required: No

Job Description: Lead Data Engineer Seize your opportunity to make a personal impact supporting the Case Management Modernization (CMM) Program.  The CMM program is an initiative to support the Administrative Office of the US Courts (AO) in developing a modern cloud-based solution to support all 204+ federal courts across the United States. GDIT is your place to make meaningful contributions to challenging projects and grow a rewarding career. The Lead Data Engineer will work as part of the CMM Data Modernization and Governance team responsible for delivering an integrated data governance, engineering, data platform, reporting, analytics, and Artificial Intelligence (AI)/Machine Learning (ML) capabilities that support operational decision-making and fulfill AO's data and analytics objectives in support of the CMM program. The successful candidate will serve as lead for designing, building, testing, and maintaining scalable data engineering components and platform services that power the CMM Program. This role delivers high-quality, secure, and performant data pipelines, transformations, ETL/ELT delivery, data management solutions, and system integrations aligned with federal standards and CMM objectives for modernization, analytics, and operational excellence. The Lead Data Engineer partners closely with the Data Architecture, Data Governance, and Migration teams to ensure engineering work is consistent, auditable, and aligned with CMM's cloud-based CI/CD standards. The Lead Data Engineer will execute the following responsibilities:

Data Platform & Architecture • Develop and implement a scalable, secure, cloud-based data platform supporting operational data, reporting, and analytics delivering cloud-based architecture (data lake, lakehouse, or data warehouse).

• Ensure alignment with federal security requirements, judiciary architecture standards, data governance policies, and application modernization initiatives.

• Engineer multi-tenant, cloud-based environments supporting hybrid/on‑premises systems, enabling SQL, NoSQL, IaaS, PaaS, distributed SQL, multi-modal, and event-driven/streaming databases.

• Design and implement auditable data integration patterns across Judiciary systems and external platforms, including the legacy CM/ECF system during the coexistence period, with pipelines integrated into Government-provided CI/CD processes.

• Implement and maintain data quality controls, validation rules, and governance-aligned data structures, coordinating with the Data Quality/Validation Engineer to sustain required accuracy and field-completeness thresholds on an ongoing basis.

• Drive continuous database and query performance monitoring and optimization, including automated performance tuning, query optimization, and indexing strategies.

• Coordinate with the Cloud Data Architect/Data Modeler to ensure engineering implementation stays aligned with the Data Architecture Blueprint and evolving data models.

• Document data platform architecture, integrations, data quality metrics, and service statistics, updating this documentation each Program Increment (PI).

• Partner with DevSecOps/CI-CD engineering to ensure data pipelines are built, tested, and deployed through Government-provided CI/CD tooling.

• Escalate and help resolve technical risks, defects, and dependencies affecting data engineering delivery across the CMM program.

• Support development of the Data and Technology Enabler Roadmap across data engineering, reporting/analytics, and AI/ML use cases.

Data Engineering & Integration • Design and develop data ingestion, ETL/ELT pipelines for integrating data from multiple enterprise sources, and transformation logic for near-real-time and batch workloads.

• Lead the implementation and ongoing maintenance of data management solutions, including operational databases, document storage services, data models, schemas, and data access APIs.

• Provide technical direction and day-to-day oversight to the Data Engineers (ETL/ELT Pipelines) team, reviewing designs and ensuring consistent engineering standards across pipelines.

• Implement Infrastructure as Code (IaC) for database provisioning, configuration, and management to ensure consistency, repeatability, and auditability.

• Implement robust, reusable data services supporting analytics, reporting, and downstream data marts and / or gold layers.

• Collaborate with architects to implement logical and physical data models in cloud-based platforms (e.g., Snowflake, Databricks, or AWS Redshift)

• Develop and maintain high-quality, testable code using secure coding standards and best practices.

• Support the operationalization CMM Data Classification Standards, including workshops, security controls, metadata requirements, and integration into system design, procurement, and training.

• Configure metadata structures, workflows, automation, and security settings; develop ingestion processes and attribute definitions for catalog entries.

• Create training, job aids, and communications to support user adoption.

• Integrate data pipelines with cloud services, messaging, and storage components.

• Implement data quality checks, validations, and error-handling mechanisms.

• Optimize pipeline performance, scalability, and cost efficiency.

• Support CI/CD-enabled deployments, including automated testing and promotion across environments.

• Support incident resolution and root cause analysis for data pipeline failures.

• Produce and maintain technical documentation, runbooks, and workflows.

• Ensure deliverables meet federal security, governan

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