Top Data Engineer Jobs in San Antonio, TX

Design and implement data architecture, build and maintain ETL/ELT pipelines, develop data models and integrations, ensure governance and security compliance, mentor junior team members, and collaborate with stakeholders to deliver cloud-based data solutions using AWS, Azure, and GCP.

Design, build, and maintain data infrastructure, pipelines, and ETL/ELT solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality, implement security and performance tuning, collaborate with clients, and support data strategy and analytics delivery.

Lead data architecture and engineering efforts: design data models, build and maintain ETL/ELT pipelines, manage data warehouses and lakes, ensure data governance and security, mentor team members, and partner with stakeholders to translate business requirements into scalable cloud-based data solutions.

Lead design and implementation of data architecture, pipelines, and cloud-based solutions. Manage large projects, engage clients strategically, ensure data governance and security, document data models and flows, evaluate new technologies, and coach high-performing teams to drive data engineering best practices.

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 design and delivery of data engineering solutions for clients: build and maintain data pipelines and architectures, apply advanced analytics and BI tools, coach teams, engage stakeholders, validate outcomes, and optimize managed-services performance.

Lead data engineering engagements: design and build ETL/ELT pipelines, implement DataStage and cloud analytics (AWS/Redshift/S3/Glue), mentor teams, collaborate with stakeholders, and deliver scalable BI and reporting solutions.

Design, build, and maintain scalable AWS data pipelines and data lakes. Migrate legacy data to AWS, implement ETL/ELT, optimize storage and query performance, and enforce data quality, governance, and monitoring while collaborating with DBAs, data scientists, and stakeholders.

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.

Build and maintain cloud-native data pipelines and architectures (warehouse, lakehouse, streaming) to support BI, analytics, ML, and GenAI. Collaborate with data scientists and stakeholders to produce AI-ready datasets, enforce data quality and governance, optimize performance and cost, and implement ingestion, ELT, and observability practices under mentorship.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.