Principal Data Lead

eSimplicity•Columbia, MD

13d•$188,700 - $200,000•Remote

About The Position

The Principal Data Lead will lead data strategy execution, data supply chain operations, metadata maturity, governed data asset promotion governance, data quality standards, and data-domain coordination for a large-scale federal data and analytics modernization program. The program supports governed data assets, reusable analytics, dashboards, APIs, public-facing reporting, and AI-enabled services. This role will improve source onboarding, data promotion, metadata maturity, data quality, lineage, stewardship, and data-domain coordination. The Principal Data Lead will ensure data assets are discoverable, governed, documented, quality-controlled, and suitable for self-service analytics and responsible AI expansion.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, Math, or other related scientific or technical discipline. With twelve years of general information technology
14+ year's experience in software engineering, including implementing engineering best practices, iterative/continuous engineering principles
All candidates must pass public trust clearance through the U.S. Federal Government. This requires candidates to either be U.S. citizens or pass clearance through the Foreign National Government System which will require that candidates have lived within the United States for at least 3 out of the previous 5 years, have a valid and non-expired passport from their country of birth and appropriate VISA/work permit documentation
Demonstrated ability to lead enterprise data strategy, data engineering, data governance, metadata, data quality, or data platform operations in a complex analytics environment.
Experience with lakehouse or comparable data platform operations, including ingestion, transformation, schema design, optimization, jobs and workflows, pipeline reliability, data quality, documentation, and production support.
Working knowledge of distributed processing, programming and query languages, governance layers, metadata management, lineage, data cataloging, and governed data asset promotion.
Ability to define and manage data quality standards, data promotion criteria, source onboarding patterns, data contracts, stewardship practices, data-domain coordination, and reusable data product documentation.
Experience collaborating with product, engineering, BI, AI/ML, security, privacy, support, and business stakeholders to support self-service analytics, certified dashboards, public-facing products, APIs, and responsible AI readiness.
Knowledge of sensitive Government data handling, approved data-use practices, least-privilege access, privacy-aware data publication, public data controls, cell suppression, and Section 508/WCAG considerations for public-facing data products.
Ability to comply with customer-specific security, privacy, accessibility, quality, training, and data-handling requirements for assigned systems and data.

Nice To Haves

Experience supporting federal, public sector, healthcare, or other regulated data, analytics, oversight, reporting, or public transparency programs.
Experience with modern cloud platforms, lakehouse or comparable data platforms, governance layers, secure data-sharing patterns, query engines, APIs, BI tools, workflow tools, source control, CI/CD, work management tools, documentation tools, observability tools, and approved monitoring patterns.
Experience advancing metadata maturity, machine-readable documentation, data stewardship, catalog discoverability, semantic consistency, dashboard certification, public-facing publication packages, and governed data assets.
Familiarity with AI/ML data readiness, model-serving data dependencies, retrieval-augmented generation patterns, AI service governance, inference logging, AI evaluation artifacts, and metadata prerequisites for responsible AI expansion.
Hands-on experience building and optimizing data pipelines within the Databricks platform
Preferred certifications may include AWS, Databricks data platform, cloud data analytics, Certified Data Management Professional, DAMA, data governance, data quality, analytics engineering, SAFe, or related data platform credentials.

Responsibilities

Lead data strategy execution, data supply chain and metadata maturity leadership, governed data asset promotion governance, data quality standards, and data-domain coordination across source systems and data owners.
Oversee applicable coverage areas, including data platform operations, distributed processing, programming and query languages, jobs and workflows, schema design, optimization, pipeline reliability, governance layers, metadata maturity, machine-readable documentation, lineage, governed data asset promotion, analytics discoverability, and domain stakeholder engagement.
Operate and improve source onboarding, ingestion, transformation, data quality, platform operations, compute governance, connectors, endpoints, workspace administration, and documentation for reusable data assets.
Advance the program’s data trust model by defining and applying admission, promotion, ownership, stewardship, lineage, refresh, trust indicator, documentation, and retirement standards for governed data assets.
Establish and monitor data quality and processing timeliness practices, including completeness, conformance, quality pass rates, defect trends, freshness against service levels, rework drivers, schema drift events, and defect remediation.
Coordinate with data owners, source-system teams, product teams, public-facing dashboard teams, BI teams, AI teams, and approved consumers to improve data contracts, ingestion readiness, metadata standards, and downstream reuse.
Ensure metadata maturity advances as a tracked, multi-year effort and that AI use cases do not scale beyond appropriate users until supporting metadata quality, lineage, documentation, and governance are sufficient for reliable outputs.
Support governed self-service analytics, certified dashboards, public-facing products, secure data sharing, reusable APIs, modernization of legacy analytics workflows, and publication workflows through governed data assets, documentation, and data governance standards.