Top Analytics Engineer Jobs in Austin, TX

Senior AWS Analytics Engineer. Posting contains no detailed responsibilities or qualifications beyond the job title.

Build, operate, and extend data platform and application-layer services enabling secure, self-service data applications at scale. Implement provisioning, CI/CD, access control, integrations, observability, automation, and incident response while contributing to semantic layer and AI-ready tooling roadmaps.

Hands-on senior role to design, develop, and support AI, analytics, data engineering, IoT, and intelligent automation solutions. Build scalable data pipelines, API integrations, streaming and operational intelligence platforms, generative AI agents, predictive maintenance models, and Power BI reporting to improve operations, asset performance, and decision-making across the enterprise.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Design, build, and deliver a global analytics platform: manage data collection and pipelines, develop visualizations and reports, create semantic models, tune performance, collaborate with IT and business stakeholders, and implement governance for analytics products.

Build and operate the core BI platform: own ingestion, integration, modular dbt pipelines, and enterprise data models. Establish data quality, testing, governance, observability, and CI/CD. Monitor platform health, certify pipelines and data products, and mentor analytics engineers to enable trusted, self-service analytics and AI-augmented workflows.

As a Data Analyst at GigSafe, you'll manage data pipelines, analyze data using Python and SQL, and convert insights into actionable business decisions in a collaborative environment.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

Design, implement, and maintain dbt/Snowflake ELT transformations to convert raw clinical and multimodal health data into trusted, analysis-ready models; enforce data quality, lineage, testing, CI/CD, and documentation; collaborate with clinical and ML teams to harmonize ontologies and support de-identification and ETL across health systems.

Own and evolve the analytics platform: build and maintain dbt data models, govern semantic metrics in Lightdash, ensure data quality and observability, and partner with analysts and stakeholders to enable reliable self-service reporting.

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.

Lead end-to-end product analytics for New Content Experiences, partnering with Product, Design, Data Science, and Engineering to analyze large datasets, build tools, ensure data quality, and inform product strategy and decision frameworks.

As a Senior Data & Analytics Engineer, you'll lead data initiatives, manage a team, and ensure data quality while turning insights into actionable decisions across the company.

Lead analytics engineering for external data products: design and maintain scalable data pipelines and models using DBT, SQL, and Python; build dashboards; partner with product, engineering, and clients; set technical standards; mentor and grow a team while remaining hands-on.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

The Analytics Engineer will design and implement analytics solutions, maintain reporting dashboards, partner with Product on KPIs, and support marketing with analytics.

Own and build scalable analytics models, metric definitions, and dashboards (customer-facing and internal). Partner with engineering to design data flows and instrumentation (BigQuery, S3, PostHog). Translate stakeholder needs into analytics plans, triage requests, and surface insights via Looker/Observable. Set analytics stack direction and ensure data quality.

Design, build, and maintain end-to-end data models and pipelines to support CX operations, compliance reporting, and customer insights. Create dashboards and metrics, enforce data quality and governance, automate recurring workflows, and partner with stakeholders and broader data teams to deliver self-serve analytics solutions.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

As an Analytics Engineer, transform raw data into metrics, manage data pipelines, ensure data integrity, and collaborate with teams for analytics solutions.

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 and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.