Top Remote Data Engineer Jobs in San Antonio, TX

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.

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.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

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).

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Design and develop Power BI semantic models and scalable reporting solutions using Databricks Lakehouse. Build advanced SQL and DAX logic, optimize performance, enforce dataset governance, implement data quality checks, and develop predictive models. Collaborate with data engineering, refactor reports to governed semantic models, automate analytical workflows, and provide technical guidance and code reviews to junior developers.

Design, build, and maintain scalable batch and streaming data pipelines on Databricks using PySpark, Delta Lake, Unity Catalog, and cloud platforms. Troubleshoot performance and quality issues, implement data governance, collaborate with cross-functional teams, and document pipelines and data models to deliver reliable, production-ready data products.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Design, build, and maintain AWS-based data pipelines and ETL processes using Glue, Spark/PySpark and EMR. Integrate multiple data sources into data lakes and Redshift, implement DevOps CI/CD practices, and work with Kafka, Hudi, and various AWS storage and analytics services.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Design, build, and maintain cloud-native data pipelines (Ingest→Transform→Expose) using Airflow, Spark, dbt, and Iceberg. Ingest and normalize HL7/FHIR/OMOP and other structured/unstructured data for analytics and AI, implement schema versioning and governance, ensure interoperability and compliance with DoD Responsible AI and NIST frameworks, and collaborate with DevSecOps and Data Science teams to deliver production-ready datasets.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design and build a medallion architecture using dbt and Microsoft Fabric, create and maintain data ingestion pipelines, and implement governance, lineage, documentation, and quality checks to support self-serve analytics while collaborating with cross-functional teams.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

Lead enterprise data platform strategy and architecture, define data standards and governance, drive Snowflake maturity, oversee legacy data migrations, design ingestion and DataOps patterns, and enable analytics, reporting, AI, and reusable data products across the organization.

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

Build and maintain production-grade data pipelines and ELT processes using Python, SQL, Airflow, and dbt within AWS. Design optimized schemas in PostgreSQL and Redshift, leverage AI/LLMs to improve development, automation, testing, and data quality, and scale infrastructure for large healthcare datasets.

Design, build, and maintain data pipelines, ETL/ELT processes, and data warehouses. Develop complex data models, tune SQL/PLSQL, support production BI/reporting, and collaborate with analysts and architects to deliver scalable analytics solutions. Mentor teammates and participate in agile delivery and production support.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

Design, build, and maintain data warehouse infrastructure, semantic/dimensional models, metrics layer, and BI dashboards (Sigma, Power BI, Databricks). Build secure ELT pipelines, enforce governance and row-level security (Entra ID), enable self-service analytics and AI/ML integrations, triage BI issues, train stakeholders, and collaborate in Agile to deliver reliable, performant reporting and curated data products.

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.