Data Engineer (SR)

TEXAS MUNICIPAL RETIREMENT SYSTEM•Austin, TX

7d•$130,000 - $170,000•Hybrid

About The Position

The Senior Data Engineer designs, builds, and optimizes enterprise data solutions that support data-driven decision-making and operational excellence across the Texas Municipal Retirement System (TMRS). Working within the IS Department, the role partners with business stakeholders and Executive Leadership to deliver secure, scalable, reliable, and high-quality data solutions. It is responsible for data pipelines, repositories, reporting platforms, data engineering and governance standards, and the integration and operationalization of AI and machine learning capabilities. The role develops reporting and business intelligence solutions; builds cloud-based data solutions and data models; and creates, monitors, and improves ETL and ELT processes and data quality. It leads enterprise AI and machine learning initiatives and establishes model lifecycle management, governance, security, and monitoring practices. The Senior Data Engineer also translates business needs into technical requirements, leads projects and peer reviews, mentors data staff, collaborates through Agile tools, and evaluates emerging technologies. After-hours support may be needed. The position is full-time in Austin, Texas, with onsite presence required at least three days per week; hybrid work opportunities are available, but this is not a remote position.

Requirements

Bachelor’s degree in data science, computer science, information systems, engineering, actuarial science, mathematics, statistics, or a related field.
At least 7 years of experience delivering data solutions, analytics, and reporting within cloud-based environments.
Proven experience leading technical projects from intake through design, development, and implementation.
Demonstrated ability to translate business needs and stakeholder requests into complex technical requirements.
Advanced proficiency in SQL and Python for data engineering, transformation, and analysis.
Hands-on experience with advanced SQL, including Oracle PL/SQL and T-SQL, and Python.
Experience with cloud data platforms and services, including Azure and AWS storage, compute, and managed data services.
Experience designing, orchestrating, and scheduling ETL and ELT pipelines.
Experience with data warehouses, lakehouses, and dimensional modeling.
Experience with business intelligence and reporting platforms such as Power BI or equivalent.
Knowledge of data governance practices, including classification, lineage, retention, and access control.
Experience integrating machine learning models and managing their lifecycle (MLOps).
Experience with Agile delivery tools, including JIRA, Confluence, and Git-based source control.
Strong written and verbal communication skills, including the ability to explain complex technical concepts to non-technical stakeholders.
Confident, mature leadership skills and high standards of character and integrity.
Excellent interpersonal and relationship skills; able to work respectfully with different personalities and perspectives and contribute positively to a team.
Intellectual curiosity, determination, strong work ethic, and ability to grasp complex subjects quickly.
Self-driven, detail-oriented, and able to perform structured problem-solving in a collaborative team environment.
Ability to perform essential job functions safely and successfully under applicable federal, state, and local standards.

Nice To Haves

Previous implementation of data and analytics solutions using Microsoft Power BI and Azure Synapse Analytics.
Experience implementing large language models and managing model versioning, including prompt and version control, evaluation tracking, continuous monitoring, and deployment lifecycle management.
Experience sourcing data from Oracle and SQL Server systems of record.
Experience with data cataloging, master data management, or metadata management tools.
Experience with streaming or near-real-time data integration.
Experience in public sector, pension administration, or financial services environments subject to SOC or NIST-based control requirements.
Experience supporting actuarial, investment, or financial reporting data needs.
Microsoft Azure Data certifications.
Microsoft Azure AI certifications.

Responsibilities

Design, develop, and maintain reports, dashboards, and business intelligence solutions aligned with organizational objectives.
Architect, build, and optimize scalable data pipelines and cloud-based solutions using modern SaaS and PaaS platforms.
Design and maintain dimensional and semantic data models for consistent enterprise reporting and analytics.
Develop and tune queries and transformations using SQL, PL/SQL, T-SQL, and Python to support ETL and ELT processes.
Maintain reliable data repositories and ensure data lineage, documented definitions, and data integrity across on-premises and cloud environments.
Integrate data from systems of record using established data modeling standards.
Establish automated data quality monitoring, reconciliation, and pipeline observability; define and report data quality measures.
Lead enterprise AI and machine learning initiatives, including model integration and operationalization.
Establish and maintain end-to-end model lifecycle processes, including version control, validation and approval, deployment standards, monitoring for drift and accuracy, and periodic reviews with documented outcomes.
Enforce data governance standards for quality, classification, retention, and access controls in line with organizational policies and Texas records retention requirements.
Ensure AI data usage controls, including approved data sources, privacy, and secure handling of sensitive member information.
Produce control evidence for internal audits, external examinations such as SOC, and NIST-aligned security control reviews.
Translate business needs and stakeholder requests into technical requirements and lead technical projects from intake through design, development, and implementation.
Lead peer review of code, models, and reports.
Mentor mid-level and junior data staff and build analytics capability among business users.
Collaborate using Agile methodologies and JIRA, Confluence, and Git to support iterative development and delivery.
Investigate new technologies and assess their viability and usefulness in current and future projects.
Improve the process, performance, and security of data solutions and integrations.
Contribute new viewpoints and strategies to stimulate growth.
Use and integrate approved AI solutions.
Provide after-hours support as needed and perform other duties as assigned.
Follow applicable policies and procedures, including security policies.