Quality Data Engineer

Build enterprise data platforms that accelerate analytics and AI/ML. This onsite role in Spring, TX partners with teams across the organization to design, deliver, and govern modern data foundations. You’ll help ensure data quality, security, and compliance while enabling faster automated analytics, operational reporting, self-service tools, and data-driven decision making.

What you’ll do

• Lead the data engineering team supporting application projects, partnering with cross-functional stakeholders to align data engineering deliverables with project outcomes.
• Oversee development and maintenance of intricate databases, including resolving critical database incidents and leading data model creation and model conversion efforts.
• Design enterprise-wide blueprints for how data is stored, integrated, accessed, and governed.
• Design and deliver PS Quality data warehouses and data lakes that meet business quality requirements.
• Determine and apply architectural patterns such as medallion architecture, data mesh, and data fabric.
• Establish data standards and automated interoperability rules, plus data modeling standards and reusable frameworks across the organization.
• Define and implement enterprise-grade data architectures for batch, streaming, and real-time processing of large-scale structured and unstructured data.
• Build secure, scalable, high-performance data platforms for BI, advanced analytics, and AI/ML use cases, balancing performance, reliability, and cost optimization.
• Establish data governance and quality capabilities including lineage, observability, monitoring, and data lifecycle best practices.
• Maintain master data management, access controls, audits, metadata management, and data hierarchy.
• Lead enterprise data strategy aligned with business, AI, and digital transformation goals, prioritizing high-value analytics and AI opportunities using telemetry, operational, and product data.
• Support productionization of ML/AI models by working closely with Data Scientists, including MLOps workflows and scalable deployment pipelines.
• Drive roadmap planning for modern data stack adoption (cloud-native, lakehouse, streaming, GenAI-ready architectures) and influence executive-level architecture decisions.

Key responsibilities and impact

This position impacts functional project teams by leading and/or providing expert guidance, and may include participation in cross-functional initiatives. You will work on complex problems requiring in-depth evaluation of multiple factors.

• Drive data monetization, standardization, and governance frameworks.
• Translate business goals into platform capabilities that support faster automated analytics, enhanced AI/ML readiness, self-service tools, and operational reporting.
• Act as a thought leader across data engineering and AI data ecosystems, including representation in industry forums, publications, and innovation initiatives.
• Influence adoption of new technologies including GenAI, agentic systems, streaming architectures, and data mesh.

What you bring

• Education: Four-year or Graduate Degree in Computer Science, Information Systems, Engineering, Statistics/Mathematics, Machine Learning, Data Analytics, and demonstrated competence.
• Experience: 7-10 years of work experience, preferably in analytics, data science, reporting, or related areas.
• Cloud: Strong experience with AWS and Azure (data services, analytics, storage).
• Data platforms: Strong experience with Data Lakes, Lakehouse, and Data Warehousing.
• ETL/ELT & orchestration: Strong experience in ETL/ELT and pipeline orchestration.
• Programming: Python and SQL (mandatory).
• Streaming: Experience with streaming and real-time data systems.
• Modeling & governance: Experience with data modeling and governance.
• MLOps: Experience with MLOps and model deployment pipelines.
• Modern architectures: Data Mesh, Medallion, and API-driven data services.
• Good to have: Scala/Java.

Technologies

AWS, Azure, Data Lakes, Lakehouse, Data Warehousing, ETL/ELT, Python, SQL, Scala, Java, Streaming and real-time data systems, Data Mesh, Medallion, API-driven data services, GenAI, agentic systems, MLOps, MLOps workflows, Apache Spark, NoSQL, Agile Methodology.

Preferred certifications

• Data Analytics Certifications

Collaboration and working style

• Effective communication, results orientation, learning agility, digital fluency, and customer centricity.
• Supports mentoring and guidance for lower-level employees, using independent judgment and productive relationship building.

Compensation, schedule, and logistics

• Salary: USD 105,050 - 161,800 per year
• Schedule: Full time
• Shift premium: None
• Travel: 25%
• Relocation: Yes

Benefits

• Health insurance, dental insurance, and vision insurance
• Long term and short term disability insurance
• Employee assistance program
• Flexible spending account
• Life insurance
• Generous time off policies, including 4-12 weeks fully paid parental leave based on tenure, 11 paid holidays, plus additional flexible paid vacation and sick leave (US benefits overview)

Note: This job description outlines the general nature and level of work. Duties, skills, responsibilities, and qualifications may be updated or assigned by management as needed.