Top Data Engineer Jobs in San Antonio, TX

Provides hands-on technical leadership for foundational data engineering and analytics engineering. Owns multi-year data architecture, production pipelines, dimensional models, and trusted metrics spanning cloud costs, traffic, infrastructure, and AI usage. Designs new datasets integrating cost, utilization, performance, and reliability signals; establishes standards, coaches engineers, aligns stakeholders, and drives adoption across Infrastructure, Finance, Data Science, and Product Engineering.

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

Own and evolve production data systems supporting healthcare, claims, billing, analytics, and reporting. Design resilient ingestion patterns, data contracts, models, schemas, and pipelines that handle incomplete, duplicated, delayed, and changing data. Improve performance, observability, failure handling, retries, backfills, and replay safety. Collaborate with engineering, product, operations, and analytics teams while making architecture decisions and documenting technical changes.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Leads the architecture, development, and support of enterprise data warehouses, lakehouses, dimensional models, ETL/ELT pipelines, and governed data products using Microsoft Fabric and related tools. Ensures data quality, security, scalability, performance, and reliability for analytics and reporting. Partners with technical and business stakeholders, documents data solutions, guides architecture decisions, mentors team members, and drives continuous improvement of data engineering standards and practices.

Designs and maintains scalable ETL pipelines, data models, integrations, cloud databases, and healthcare data infrastructure. Ensures security, HIPAA/HITRUST compliance, reliability, monitoring, and documentation while collaborating with product, engineering, and clinical teams. Requires strong SQL and Python expertise, modern data warehousing and orchestration experience, cloud platform knowledge, and familiarity with healthcare interoperability and claims data.

Lead Data Engineer responsible for designing, maintaining, and improving data pipelines and platform infrastructure across ingestion, streaming, transformation, and delivery. The role uses Airflow, Spark, Iceberg, Kafka, Flink, dbt, Terraform, and GCP. Responsibilities also include defining data contracts, documenting systems, optimizing performance, reviewing code, supporting access controls and compliance, and collaborating with stakeholders and teammates.

Maintain and administer Microsoft SQL Server databases supporting healthcare research administration, compliance, reporting, and operations. Design schemas and tables; develop and optimize stored procedures, queries, scripts, utilities, and reports; manage scheduled jobs; monitor performance, security, backups, recovery, and data integrity; troubleshoot database and application issues; coordinate with web developers; document solutions; and provide technical recommendations to research leaders.

Leads data engineering teams in designing, developing, and supporting scalable batch and streaming data solutions. Responsibilities include data pipeline development, architecture, governance, security, platform optimization, production support, technical mentorship, stakeholder collaboration, and delivery leadership. The role requires hands-on engineering with Python or similar languages, modern data technologies, cloud platforms, DevOps practices, and people-management responsibilities.

Build and operate Grassroots Carbon’s unified data platform, including AWS-based storage, production ingestion pipelines, scheduled datasets, integrations, model deployment, monitoring, security, and access controls. Migrate and reconcile historical PastureMap data while establishing lineage, shared identifiers, validation, and reproducible workflows. Partner with science, product, backend engineering, and operations teams to productionize models and deliver reliable data services supporting soil carbon modeling, grazing management, and rancher-facing products.

Lead the modernization of a healthcare data platform from an MSSQL XML object store to a scalable AWS architecture. Evaluate AWS storage and query services, design SQL reporting layers, architect data migration and metadata strategies, optimize performance and cost, and build CI/CD and infrastructure-as-code pipelines. Partner with business stakeholders, analysts, and C#/.NET engineers to deliver accessible data models and reliable integrations. Document architecture, data flows, and migration processes while supporting healthcare data and compliance requirements.

Supports the design and development of scalable end-to-end data pipelines and cloud data platforms. Responsibilities include improving data reliability and quality, implementing data management practices, supporting automation and process improvements, ensuring secure and cost-effective solutions, and recommending system enhancements. The intern collaborates with global teams and gains hands-on experience in data engineering, project coordination, and innovation management.

The Principal Data Engineer designs and builds scalable cloud-native data platforms, pipelines, frameworks, and governed data products supporting analytics, reporting, AI, and operational workloads. Responsibilities include Databricks, Spark, Delta Lake, Azure, SQL Server, batch and streaming processing, data integration, performance optimization, governance, modernization, troubleshooting, architecture reviews, and mentorship. The role provides senior technical leadership without direct people management and supports insurance-domain data across policy, claims, billing, finance, and regulatory functions.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

Develops scalable data and software systems using Python and Apache Spark. Designs APIs, data models, ETL workflows, orchestration, and data interactions while optimizing performance, scalability, integrity, and security. Builds user interfaces, contributes to automated testing, applies Agile practices, and supports CI/CD delivery. The role requires strong software engineering, distributed computing, analytical, and communication skills, with AWS, R, Databricks, and SAS experience preferred.

Lead production of federal species range maps for marine and anadromous taxa. Aggregate, normalize, transform, and validate spatial data; analyze habitat intersects; coordinate iterative reviews with FWS and NOAA SMEs; prepare QA documentation; and secure government sign-off for map deliverables.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

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.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, cloud data infrastructure, and complex data models. Supports analytics partners, develops data warehouse solutions, tunes SQL and database performance, troubleshoots production issues, and delivers end-to-end data engineering projects. The role collaborates with architects, analysts, developers, and business stakeholders, applies big data technologies, supports agile delivery, and mentors team members.

Design and build a durable data storage platform that preserves source disagreements, provenance, uncertainty, historical versions, and evidence. Develop result and evidence stores, lineage tracking, versioned rules and configurations, reproducible decision records, and point-in-time entity views. Collaborate with entity-resolution and machine-learning teams to support probabilistic outputs. The role requires production data engineering experience, strong Python and SQL, PostgreSQL expertise, U.S. citizenship, and an active Secret clearance.

Designs and implements scalable software solutions, develops maintainable features, resolves bugs, optimizes performance, and writes automated tests. Participates in architecture discussions, code reviews, Agile planning, CI/CD, observability, and adoption of AI-assisted engineering tools. Collaborates with QA, Product Management, and DevOps to deliver software across web, desktop, cloud, and IoT environments. The role is remote for candidates in the Seattle or Los Angeles metropolitan areas, with quarterly travel to Los Angeles.

Lead the Ads Signals & Measurement data engineering team across conversion and attribution, measurement foundations, audience onboarding, and identity. Set cross-organizational technical direction, resolve scalable data architecture decisions, mentor engineers, and partner on platform-wide initiatives. The role requires expertise in modern data stacks, advertising technology data flows, privacy and governance, distributed systems, and emerging AI technologies.