> Markdown version of [/jobs/ext/1233838-principal-data-lead](https://www.wearedevelopers.com/jobs/ext/1233838-principal-data-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Principal Data Lead

- **Company:** ESimplicity, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $188,700.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Information Systems, Computer Programming, Continuous Integration, Data Control, Information Engineering, Data Governance, Data Infrastructure, Data Security, Query Languages, Digital Assets, Distributed Computing Environment, Metadata, Meta-Data Management, Metadata Standards, Software Engineering, Web Content Accessibility Guidelines, Workflow Management Systems, Data Logging, Data Processing, Retrieval-Augmented Generation, Grafana, Data Strategy, Documentation System, Information Technology, Data Management, Software Version Control, Data Pipelines, Databricks
- **Published:** July 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=82db634159f8f24b

## About the Role

* 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.

Preferred qualifications

 * 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, connectors, 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.

## Description

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.

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., eSimplicity supports a remote work environment operating within the Eastern time zone so we can work with and respond to our government clients. Expected hours are 9:00 AM to 5:00 PM Eastern unless otherwise directed by manager.

Occasional travel for training and project meetings. It is estimated to be less than 5% per year.


## Related Videos

- [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source)
- [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata)
- [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads)
- [Parquet, Delta, Iceberg & Ducklake - An introduction for developers](https://www.wearedevelopers.com/videos/100075-parquet-delta-iceberg-ducklake-an-introduction-for-developers)

## Related Articles

- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)