Data Engineering Supervisor

Texas Health & Human Services Commission•Austin, TX

54d•Onsite

About The Position

Join the Texas Health and Human Services Commission (HHSC) and be part of a team committed to creating a positive impact in the lives of fellow Texans. At HHSC, your contributions matter, and we support you at each stage of your life and work journey. Our comprehensive benefits package includes 100% paid employee health insurance for full-time eligible employees, a defined benefit pension plan, generous time off benefits, numerous opportunities for career advancement and more. Explore more details on the Benefits of Working at HHS webpage. We are seeking a highly organized and technically skilled Data Engineering Manager to lead and mentor a team of 10-20 data engineers. In this leadership role, you will be responsible for ensuring the successful delivery of data projects, maintaining data quality and security, managing team operations using agile methodologies, and driving cross-functional collaboration. This position requires a strong understanding of engineering best practices, process management, and team leadership, with a clear focus on security compliance, operational excellence, and AI-readiness. Must be willing to work onsite at an Austin, TX HHS office three (3), or more, days per week.

Requirements

Strong knowledge of data security, quality assurance, and compliance best practices.
5+ years of experience in data engineering, with at least 2 years in a leadership or management role.
Proven experience managing agile teams and performing Scrum Master duties.
Experience using Project Portfolio Management (PPM) tools for tracking and planning.
Experience integrating AI into data workflows, including anomaly detection, metadata enrichment, and prompt engineering.
Demonstrated ability to lead recruitment, staff development, and performance management activities.
Excellent communication and presentation skills, with the ability to clearly explain complex technical topics to non-technical audiences.
Required minimum of 3 years of experience managing a technical team of data developers
Required Minimum of 7 years of experience in a software development team with some of that focused on analytics

Nice To Haves

Agile certification preferred.
Security certification is preferred.
Preferred 4 years of experience with modern data ingestion platforms (e.g., Snowflake, dbt, OpenFlow, Informatica).
Preferred 2 years of experience with cloud infrastructure (Azure, AWS, or GCP).
Preferred 1 year of experience with CI/CD processes, DevOps, and/or data pipeline automation.
Bachelor's degree in computer science, or related discipline, is preferred.

Responsibilities

Team Leadership & Agile Delivery - 20% Lead and manage a team of 10-20 data engineers, fostering a high-performing, collaborative team culture.
Act as Scrum Master, facilitating agile ceremonies including sprint planning, standups, retrospectives, and backlog grooming.
Monitor team activities across multiple active projects, ensuring consistent progress, balanced workloads, and accountability.
Conduct regular performance reviews, provide coaching and mentorship, and support professional development.
Approve and monitor time sheets to ensure compliance with reporting requirements.
AI Integration & Innovation - 20% Lead efforts to integrate AI/ML capabilities into data engineering workflows using platforms such as Snowflake Cortex, Azure AI, and Informatica CLAIRE.
Promote the use of generative AI tools (e.g., ChatGPT, Gemini, Copilot) for documentation, summarization, and automation.
Support the development of AI-enabled metadata management, schema mapping, and anomaly detection.
Stay current with emerging AI trends and tools, and guide the team in responsible experimentation and adoption.
Project & Workflow Management - 20% Design, organize, administer, and document all team work in Jira, including tickets, epics, releases, and reporting dashboards.
Manage project tracking in Project Portfolio Management (PPM) tools, aligning work with business priorities and delivery timelines.
Ensure project deadlines are met, releases are complete, and dependencies are resolved in a timely manner.
Develop and implement robust processes for intake management, SDLC workflows, and production support.
Data Governance, Quality & Security - 15% Ensure that all data engineering processes meet data quality standards, compliance requirements, and security protocols.
Monitor for adherence to organizational security policies, and ensure all team members follow established security best practices.
Collaborate with internal stakeholders (e.g., InfoSec, compliance, audit) to maintain and improve data governance practices.
Implement AI governance practices including audit trails, sandboxing of AI-generated outputs, and role-based access controls (RBAC).
Stakeholder Engagement & Communication - 15% Collaborate directly with customers and stakeholders to define new project intakes, clarify requirements, and refine priorities.
Create presentations that communicate plans, updates, risks, and technical overviews; deliver these to technical and business audiences.
Provide regular written and verbal status updates to leadership, stakeholders, and project sponsors.
Recruitment & Training - 10% Manage the recruiting process for new team members: post job openings, screen resumes, conduct interviews, and make hiring recommendations.
Develop and maintain training documentation to support onboarding, internal knowledge sharing, and process standardization.
Encourage team participation in AI learning paths, certifications, and community engagement.