Top Data Engineer Jobs in Houston, TX

Leads development of secure, scalable data platforms for KYC and risk assessment. Designs and delivers enterprise data applications, frameworks, APIs, and processing systems; mentors engineers; drives AI-assisted development practices; and ensures secure, reliable, and compliant engineering workflows. The role uses cloud-native technologies, large-scale data processing, databases, observability, orchestration, modern data platforms, and emerging LLM infrastructure while communicating with senior leaders.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Designs dashboards, reports, interactive analytics applications, and web-based data experiences. Translates business questions into intuitive visualizations, identifies trends and anomalies, establishes dashboard standards, and partners with stakeholders and application developers. The role also integrates AI-enabled capabilities, improves existing analytics solutions, and promotes usability, adoption, and data-driven decision-making across the organization.

Lead enterprise data engineering initiatives by building and modernizing pipelines across mainframe, data warehouse, data lake, and cloud environments. Responsibilities include data ingestion, transformation, governance, validation, troubleshooting, legacy process modernization, project ownership, standards development, and mentoring. The role collaborates with actuarial, enterprise analytics, and insurance business teams to deliver reliable data for reporting, analytics, and downstream models.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Lead architecture and engineering for secure, scalable data platforms, pipelines, backup, recovery, immutable-data protection, and recovery assurance. Define data models, build AWS cloud-native automation, embed SRE and observability practices, implement security controls, and deliver operational analytics. Lead technical standards, code reviews, mentoring, stakeholder alignment, incident response, and cross-team delivery while ensuring measurable reliability, compliance, and RPO/RTO outcomes across relational and NoSQL systems.

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.

Staff Data Engineer leading architecture and technical direction for real-time and batch motorsports data platforms. Responsibilities include telemetry ingestion, streaming systems, data quality, observability, cloud platforms, reliability, incident response, technical standards, roadmap planning, stakeholder influence, and mentoring. The role delivers scalable solutions supporting race-day operations and cross-functional analytical products.

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.

Design, build, and optimize scalable data pipelines using Databricks, PySpark, SQL, Delta Lake, and lakehouse architecture. Collaborate with analytics, product, and engineering teams while developing batch and streaming workflows, integrating data warehouse systems, and using cloud platforms and orchestration tools such as Azure, Airflow, ADF, or Prefect.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Designs Azure-based data models, simulations, test environments, automated testing frameworks, and performance validation solutions for healthcare data pipelines. Develops test data generators, validates ETL and HL7 FHIR transformations, supports data quality and reliability, and collaborates with engineers and architects on design and code reviews. The role also provides technical guidance on testing architecture, CI/CD, infrastructure as code, resiliency, and large-scale cloud data processing.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.

Design, build, and maintain scalable batch and streaming data pipelines using Databricks, PySpark, Python, SQL, Spark, and Delta Lake. Develop transformations, orchestration workflows, data models, validation processes, and curated data products. Troubleshoot performance and quality issues, apply governance and security practices, document solutions, and collaborate with architects, data scientists, DevOps teams, analysts, and clients.

Design and implement data infrastructure, custom extractors, ETL pipelines, data models, and visualization prototypes for industrial clients. Collaborate with solution architects, subject matter experts, product teams, and stakeholders to deliver reliable data solutions using Python, Spark, SQL, Kafka, REST APIs, and Docker. The role requires experience in oil and gas or manufacturing, client-facing communication, and onsite travel from a hybrid Houston location.

Designs, builds, and maintains Snowflake-based enterprise data warehouse solutions, including data architecture, ETL/ELT pipelines, security, deployment, data quality, modeling, and performance optimization. Partners with integration, analytics, governance, business intelligence, and architecture teams. Supports production operations, resolves ingestion and transformation issues, develops AWS-to-Snowflake scripts, documents implementations, and translates business requirements into scalable data solutions.

Designs, develops, and maintains scalable ETL/ELT pipelines and data platform capabilities for high-volume healthcare data. Responsibilities include complex SQL development, Airflow orchestration, dbt transformations, data quality frameworks, pipeline monitoring, troubleshooting, performance optimization, and documentation. The role collaborates with Data Operations, Product, Analytics, Data Science, and clinical teams to deliver reliable data for analytics, reporting, product features, and AI/ML initiatives.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Lead the technical direction, architecture, reliability, observability, and modernization of a cloud-based data engineering platform. Design scalable pipelines and reusable frameworks supporting analytics and AI, improve data quality, and establish testing, code review, CI/CD, and engineering standards. Drive adoption of AI-assisted development tools and build agentic workflows across planning, coding, testing, and deployment. Partner with machine learning, MLOps, Analytics Engineering, and business stakeholders in a highly autonomous senior individual contributor role.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs, develops, maintains, and improves software applications and business processes. Responsibilities include full lifecycle development, troubleshooting, performance and reliability improvements, CI/CD collaboration, requirements analysis, technology research, package maintenance, portfolio planning, and mentoring junior engineers. The role requires independent work, cross-functional collaboration, and strong software development, documentation, and problem-solving skills.

Build and maintain scalable Python-based data pipelines and cloud-native solutions supporting U.S. Army financial-system modernization. Develop ETL/ELT workflows using AWS Glue, Lambda, S3, and CloudWatch; integrate APIs and diverse file formats; improve data reliability, performance, security, and governance; implement testing and monitoring; and collaborate with engineers and stakeholders in an Agile environment.

Build and own scalable data platforms and ingestion pipelines using BigQuery, dbt, and modern ingestion tools. Design batch and CDC workflows, improve reliability and observability, implement governance and access controls, and support analytics and AI agents. Develop Python services and internal tooling, contribute to architecture and lakehouse experiments, collaborate with stakeholders, and mentor engineers. The role requires strong SQL, Python, cloud infrastructure, testing, CI/CD, and AI-assisted development experience.

Develop and maintain data pipelines, backend programs, and integrations in Azure Government Cloud. Transform and cleanse data, evolve scripts and notebooks, manage relational databases, support data quality, document technical processes, and collaborate with engineers, geospatial experts, scientists, and government clients. The role also involves project coordination, status reporting, client travel, and compliance with government background and security requirements.

Designs, develops, maintains, and optimizes complex data systems, SQL/ETL processes, reporting solutions, and .NET applications supporting pharmacy benefit management. Responsibilities include database performance tuning, data warehousing, troubleshooting pharmacy claims, technical specification development, software testing, cloud integration, stakeholder collaboration, Agile participation, and mentoring. The role requires strong SQL Server, dimensional modeling, cloud database, Python, PowerShell, and healthcare data standards expertise.