Top Data Engineer Jobs in Austin, TX

Build and scale data pipelines, APIs, integrations, and data models supporting CRM personalization, segmentation, audience activation, and campaign measurement. Ensure pipeline reliability through monitoring, alerting, and logging; automate workflows; document systems; participate in architecture and code reviews; and mentor junior engineers. Collaborate with data engineering, data science, and CRM teams to improve personalized email, push, and SMS engagement for millions of users.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Designs, builds, and operates scalable data pipelines, services, and products supporting clickstream and behavioral data. Responsibilities include data modeling, API and system design, automated quality frameworks, observability, governance, reliability, security, and performance optimization. The role collaborates with engineering, product, analytics, and domain teams, translates complex requirements into production solutions, and applies AI/ML-enabled tools and workflows. It also contributes to technical decisions, data-driven improvements, and operational excellence.

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 scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Designs and leads enterprise data engineering solutions, including cloud data pipelines, repositories, models, ETL/ELT processes, reporting platforms, and data governance. Integrates and operationalizes AI and machine learning models, establishes MLOps lifecycle controls, and ensures data quality, security, lineage, and compliance. Translates stakeholder needs into technical requirements, leads projects, mentors staff, and supports analytics capabilities across the organization.

Lead the technical strategy and architecture for a modern, real-time enterprise data platform. Design scalable data models, ingestion strategies, governance, security, compliance, and cost-optimization initiatives. Drive the data roadmap across multiple squads, make strategic technical decisions, collaborate with business stakeholders, troubleshoot complex issues, and mentor engineers. The role requires hands-on leadership in data architecture, real-time platforms, and enterprise data strategy.

Build and maintain scalable data pipelines, models, cost allocation systems, metrics, dashboards, and analytical tools for PlayStation’s global infrastructure. Connect infrastructure, financial, engineering, and product data to analyze cost, utilization, capacity, and unit economics. Ensure data quality, testing, observability, documentation, and reproducibility while partnering across engineering, product, finance, and operations teams.

Design, build, and optimize scalable batch and streaming data pipelines using Fivetran, Databricks, Spark, Azure, Kafka, and Kubernetes. Develop source integrations, improve data delivery and infrastructure scalability, monitor and troubleshoot production workloads, and ensure SLA compliance. Contribute to architecture and technology decisions, automate deployments and monitoring, collaborate across teams, and mentor junior engineers supporting intelligent manufacturing analytics.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

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).

Architect and lead enterprise data quality, observability, and validation frameworks for lead management systems. Own data integrity across ingestion, processing, and delivery pipelines; define data contracts, governance standards, SLAs, dashboards, and alerting. Diagnose systemic issues across distributed data flows, drive scalable architectural solutions, advise senior stakeholders, and mentor data engineers. Partner cross-functionally with Engineering, Product, and Data Science to improve reliability, customer fulfillment, and marketplace performance.

Build and maintain scalable Microsoft Fabric data pipelines, ingestion frameworks, notebooks, and dimensional data models. Optimize Delta Lake storage, Power BI semantic models, Direct Lake performance, refresh strategies, and Fabric capacity usage. Ensure data quality, observability, SLA reliability, and scalable client onboarding. Collaborate with BI developers, analysts, and stakeholders in an agile environment while following Git, CI/CD, code review, and SDLC practices.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Contributes to building and optimizing a large-scale data lake and reliable data pipelines for cybersecurity analytics. Responsibilities include data modeling, distributed processing, workload optimization, monitoring, observability, CI/CD, code reviews, and supporting MLOps. The role collaborates with senior engineers to improve processes and deliver cost-effective cloud data solutions.

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.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Supports ETL pipeline implementation, data ingestion, data quality validation, schema and RBAC configuration, database performance testing, interoperability testing, and production migration activities. Maintains metadata registries, documents ingestion defects and remediation recommendations, validates post-migration data, and maintains database scripts and pipeline code in version control. The position is fully remote and supports a federal data migration program.

Develop and deploy Databricks Delta Live Tables ETL pipelines in a cloud lakehouse environment. Aggregate, normalize, transform, and validate geospatial data from authoritative sources. Document ingestion results, data quality defects, remediation actions, and test outcomes. Maintain version-controlled code with peer reviews, support GIS-based spatial validation, and assist with interoperability testing. The role supports federal endangered-species data integration and requires proficiency in Python or Scala, SQL, data quality validation, schema documentation, and geospatial data concepts.

Design, build, and maintain scalable batch and streaming data pipelines, cloud infrastructure, and the Enterprise Data Hub. Partner with stakeholders to productionize ingestions, ensure data quality, implement metadata/catalog solutions, mentor junior engineers, and support analytics and ML workloads using cloud and big-data tooling.

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.

Data Engineer II will modernize BlueLabs’ data platform by leading Kimball dimensional modeling and legacy warehouse migrations, building reliable production data pipelines, and enforcing data quality standards. The role requires advanced SQL, Python, and dbt expertise, hands-on Snowflake and Redshift experience, and ownership of testing, documentation, observability, and scalable architecture. The engineer will collaborate cross-functionally, guide Data Engineer I team members, contribute to architectural decisions, and translate business logic into maintainable, cloud-agnostic data layers.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Designs, builds, and optimizes scalable data pipelines, ETL workflows, integrations, and reporting solutions. Develops SQL, Python, and Spark data-processing solutions using Azure Data Factory and Microsoft Fabric, while monitoring production reliability and performance. Collaborates with stakeholders, participates in code reviews, documents technical solutions, mentors junior engineers, and improves engineering practices. Requires strong data modeling, cloud data platform, Git, communication, and problem-solving skills.

Design, develop, and maintain data pipelines, ETL processes, integrations, data infrastructure, and complex data models. Build and support data warehouses and analytics solutions using SQL, PL/SQL, Oracle, Snowflake, and cloud technologies. Collaborate with analysts, architects, developers, data scientists, and business stakeholders throughout the delivery lifecycle. Provide production support, troubleshoot issues, tune database performance, and contribute to data-driven reporting and AI tools.