Top Data Engineer Jobs in San Antonio, TX

Design, build, and maintain scalable ETL/ELT pipelines, data warehouses, and data lakes. Ensure data quality, governance, and security while enabling BI and analytics. Provide technical leadership, mentor junior engineers, and collaborate cross-functionally to drive architecture and implementation.

Lead data science and analytics initiatives for healthcare, overseeing model development, data integrity, and large-scale automated analyses. Provide actionable insights, mentor teams, collaborate with cross-functional partners, evaluate payment integrity campaigns, and present findings to stakeholders to optimize revenue, quality, consumer experience, and healthcare costs.

Lead design and scaling of ELT pipelines and analytics schemas on Snowflake. Build Looker views, CI/CD automation, and orchestration workflows (Prefect/Airflow). Ensure production reliability with monitoring (Datadog, Monte Carlo), partner with cross-functional teams, and provide technical leadership including code reviews, testing, and end-to-end ownership of data integrity and platform trustworthiness.

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

Leads data engineering strategy, architecture, delivery, support, and lifecycle management for secure, scalable cloud and hosted data solutions. Collaborates with technical and business stakeholders to deliver complex datasets, establish enterprise data standards, manage data engineering teams, and oversee maintenance, change management, capacity planning, business continuity, disaster recovery, and regulatory security requirements. The role also evaluates third-party tools and supports Azure DevOps, SDLC, CI/CD, and technical innovation.

Design, build, and optimize scalable cloud data pipelines and infrastructure. Maintain and refactor SQL and ETL processes, automate data workflows, ensure data quality/security, and troubleshoot complex issues. Provide technical leadership and mentor junior engineers while collaborating with stakeholders to translate requirements.

Serve as the technical owner for customer-facing data workflows and integrations: build pipelines, write SQL and Python scripts, deploy AI-powered workflows, manage APIs and Snowflake ingestion, and translate repeatable solutions into product requirements while supporting customer success and internal engineering.

The Senior Data Engineer will design and scale cloud-based data platforms, ensuring quality and performance while collaborating with cross-functional teams to deliver actionable data solutions.

The Senior Data Engineer will develop data engineering solutions, manage ETL workflows, and establish a new data practice while collaborating with stakeholders.

The Lead Data Platform Engineer will design and build Virtuous's data platform, ensuring it is secure, scalable, and AI-ready. Responsibilities include architecting access controls, optimizing performance, and enabling self-service analytics while maintaining governance and compliance standards.

The Data Architect will design scalable data models, transform raw data into actionable insights, and deploy analytics solutions for healthcare providers, with a strong emphasis on SQL proficiency and cloud technologies.

Design, build, and maintain scalable ETL/ELT data pipelines and analytical platforms on AWS and Databricks. Migrate legacy workflows to cloud-native services, implement data lake and catalog solutions, enable AI/RAG/vector search, ensure FedRAMP/NIST compliance, optimize performance, and mentor junior engineers while collaborating in Agile teams.

Design, build, and operate Alpaca's scalable data platform and lakehouse infrastructure. Build low-latency streaming and CDC ingestion, batch pipelines landing in Iceberg, and tooling for BI and AI consumers. Manage IaC, Kubernetes deployments, monitoring/incident response, and partner with DevOps and analytics teams to meet growing data requirements.

Lead Data Engineer responsible for defining architecture, engineering standards, and best practices for the enterprise data platform, collaborating with distributed teams for modernization and scalability.

Design, build, and maintain scalable, reliable data systems and pipelines. Partner with Product, Sales Ops, and Finance to deliver analytics and self-service tooling. Maintain SLOs, improve pipeline reliability, implement testing and documentation, and contribute to data platform strategy and operations.

Design, build, and own end-to-end data systems and pipelines powering payments, fraud response, and reporting. Implement streaming and batch integrations, APIs, and workflow orchestration. Define data correctness with monitoring, alerting, and tests. Create reusable patterns and collaborate with stakeholders to clarify requirements and tradeoffs.

Design, build, and operate a scalable, secure data platform. Architect end-to-end data pipelines, manage Snowflake performance and costs, implement CI/CD and observability, ensure data governance and HIPAA alignment, integrate diverse sources, mentor teammates, and support analytics and business users.

The Senior Data Engineer designs and optimizes scalable data pipelines using Azure services, collaborates with teams, mentors others, ensures data quality, and drives innovation in data architecture.

Design and own Ceresti’s end-to-end data architecture, including secure raw-data storage, validated file and API ingestion, Postgres pipelines, and curated analytics layers. Establish governance for HIPAA-protected data, data quality, lineage, observability, and access controls. Build ML feature tables and embedding capabilities, collaborate on data contracts, support Agile delivery, and mentor engineers on SQL and data-system design.

Design, build, and maintain scalable cloud and hybrid data platforms, ETL/ELT pipelines, and data models. Implement CI/CD and DevOps practices, ensure data quality, security, and governance, optimize production pipelines, lead modernization and integration efforts, and mentor junior engineers.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Designs and delivers a global analytics platform supporting reporting, visualizations, self-service analytics, and embedded data products. Responsibilities include data collection and processing, semantic modeling, warehouse and pipeline development, query optimization, governance, analytics enablement, and stakeholder collaboration. The role provides actionable insights from complex travel data and builds integrated analytics applications using cloud platforms and business intelligence tools.

Designs and maintains Snowflake-based cloud data pipelines and lake architectures for asset-based finance and mortgage servicing analytics. Leads legacy-system migrations, ETL/ELT development, data validation, reconciliation, and financial integrity controls. Translates business requirements into Jira epics and user stories, supports project planning, testing, cutover, and go-live readiness, and coordinates with business, finance, operations, and technical stakeholders.

Design and scale Zip’s enterprise data and AI platform across batch, streaming, structured, and unstructured workloads. Build reliable pipelines, AI-ready data products, semantic models, vector and hybrid search, and RAG capabilities. Establish standards for modeling, testing, governance, security, observability, and automation. Partner with engineering, data science, product, and business teams while mentoring engineers and influencing platform architecture.

Design and build modern GCP lakehouse architectures, including Python and Spark pipelines, BigQuery ingestion, Kafka CDC, Delta Lake and Iceberg UniForm integrations, Delta Sharing endpoints, Snowflake and Databricks connectivity, data lineage, governed access controls, and semantic-layer alignment. Collaborate cross-functionally, troubleshoot full-stack issues, and deliver reusable data-sharing adapters and end-to-end data features.