Top Data Engineer Jobs in Houston, TX

Lead Managed Data & Analytics engagements to build modern data ecosystems, convert insights into strategic opportunities, and manage client accounts. Oversee project planning, budgeting, execution, and delivery while mentoring teams, enforcing quality standards, and identifying growth opportunities.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior team members, uphold standards, build client relationships, and align data solutions with business needs.

Design, implement, and manage advanced data ecosystems and scalable pipelines. Analyze complex problems, deliver managed data and analytics solutions, maintain data governance, mentor junior team members, and build client relationships to drive actionable insights.

Design, build, and maintain scalable data infrastructure, pipelines, lakes/warehouses, and ETL processes using cloud and big-data tools. Ensure data quality, security, and performance; collaborate with clients to deliver actionable insights and support data strategy.

Lead design and implementation of data architecture, data models, ETL/ELT pipelines, and data warehouses/lakes. Manage data governance, security, and cloud-based solutions across AWS/Azure/GCP. Coach and mentor team members, partner with stakeholders to translate business needs into technical solutions, and oversee project planning, delivery, and quality.

Develop and maintain data pipelines and ETL processes, optimize SQL and database performance, build BI reports and dashboards, apply data architecture and analytics to support client decision-making, and collaborate with teams in regulated environments while improving data quality and processes.

Lead design and implementation of data architecture and large-scale data engineering projects. Develop data pipelines, models, and flow diagrams while ensuring governance and security compliance. Advise clients strategically, manage teams, and drive operational excellence and innovation.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms (AWS/Azure/GCP). Develop data models, ensure governance and security compliance, analyze complex problems, mentor junior staff, build client relationships, and enhance technical skills to deliver high-quality data solutions.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop and manage data lakes and warehouses, apply data architecture and dimensional modeling, validate data quality, implement security best practices, and collaborate with clients to deliver analytics and data strategies. Mentor peers and manage growing client responsibilities as a Senior Associate.

Lead design and delivery of cloud-based data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and data security. Manage projects, mentor team members, ensure governance compliance, collaborate with stakeholders, and optimize cloud infrastructure for performance, cost, and scalability.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, translate stakeholder requirements into technical designs, ensure data governance and security, develop data models and pipelines, advise clients strategically, and coach teams to deliver operational excellence.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Lead data engineering efforts within technology consulting: design and maintain data architectures and pipelines, apply advanced analytics (AWS/Redshift), implement BI solutions, coach teams, engage stakeholders, validate outcomes, and optimize managed services performance.

Lead data engineering projects and teams to design, build, and maintain ETL/ELT pipelines, data architectures, and analytics platforms. Implement cloud-based solutions (AWS/Redshift/S3/Glue), optimize performance, deploy BI and visualization tools, mentor staff, manage client engagements, and ensure project delivery and technical standards.

Design, implement, and optimize large-scale data processing on Databricks and Spark. Build ETL pipelines, integrate data across AWS services (Redshift, RDS), orchestrate workflows with Airflow, and ensure data warehousing and engineering best practices. Lead and mentor development teams and collaborate with cross-functional stakeholders to drive data strategy and execution.

Build and maintain ETL pipelines with Alteryx Server, support data warehouse integration and migrations, perform data cleansing/transformation, optimize SQL/Python pipelines, and produce Power BI reports as needed while collaborating with stakeholders.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.