Top Senior Data Engineer Jobs

Lead teams to design, build, and operate scalable, AI-ready data pipelines and ecommerce data products (especially Shopify). Build semantic layers, curated data marts, and data models; ensure data quality, monitoring, and performance. Collaborate with data scientists, analysts, and governance to support feature engineering, ML datasets, and reliable downstream consumption.

Build and own Plug's governed data layer: dbt transformation models and MetricFlow definitions, ingestion pipelines (Fivetran, HubSpot, OEM APIs), ML feature store maintenance, data quality monitoring, and support self-service reporting. Collaborate with engineering and ML teams, participate in incident response, and raise data engineering standards.

Lead design and evolution of a company-wide data platform spanning streaming, batch, warehousing, orchestration, governance, and ML enablement. Deliver core platform capabilities, reliable real-time pipelines, shared tooling, and scalable infrastructure. Drive cross-team initiatives, champion golden paths, improve reliability/cost/observability, ensure regulatory compliance, and mentor senior engineers while influencing technical strategy and standards.

Build and scale optimized data pipelines and data engine architecture to run ML models, reduce annotation costs, integrate foundation models for labeling and QA, use software/hardware-in-the-loop testing, and collaborate with DoD customers to understand requirements and support field events.

Own design, build, and reliability of a cloud-native data lakehouse (Bronze->Silver->Gold). Build scalable ETL/ELT pipelines (ADF, Airflow), optimise Snowflake, develop dbt models, embed data quality/observability (Elementary), manage ADLS Gen2 and Azure serverless components, and drive infra-as-code, CI/CD, and cross-team collaboration.

The Senior Data Engineer will build and maintain data pipelines, distributed ETL/ELT workflows, and data storage systems while collaborating with various teams to optimize data quality and performance.

Design, build, and optimize high-throughput data pipelines and infrastructure for Defense Health. Develop RESTful APIs and cloud data solutions (AWS), apply DevSecOps and TDD, use Python, Trino, Spark, dbt, and Rancher Kubernetes, and collaborate with stakeholders to deliver secure, production-grade analytics services.

Design, build, and optimize scalable ETL/ELT pipelines and cloud data platforms. Integrate data from SQL Server, Yardi, Salesforce and APIs into AWS (S3, Redshift). Collaborate with analytics, data science, and business teams to deliver trusted datasets, ensure data quality and governance, and implement DevOps, CI/CD, and infrastructure automation for operational excellence.

Design and optimize scalable data pipelines and models at Fabletics, collaborating with teams to enhance data accessibility and performance for business insights.

Design, build, and maintain an internal data platform enabling secure, governed access for engineering and analytics. Partner with architects, implement data pipelines and automations, enforce security and compliance, support platform services, recommend architecture improvements, mentor teams, and drive adoption of scalable, self-service data capabilities.

Design, build, and operate Brex's data enablement platform (warehouse, streaming, replication, orchestration, observability, governance). Partner with product, data science, and analytics teams to deliver data-backed products, improve data competency, and maintain resilient, on-call data architectures.

Own ingestion, transformation, validation, and normalization of clinical and EHR data pipelines (HL7 v2, FHIR, CDC) into performant Snowflake and MySQL schemas. Build and monitor AWS-based pipelines, ensure data quality and PHI-compliant handling, and support downstream ML and application needs across enterprise integrations.

Lead design and implementation of scalable Databricks ETL/ELT pipelines and data architectures for marketing analytics, manage 1-2 projects, liaise with stakeholders, ensure data quality, monitoring, and delivery of analytics-ready datasets.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.

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.

Design, build, and maintain scalable real-time and batch data pipelines and platform architecture. Ensure data quality, enable BI/AI/ML productionization, mentor engineers, and deploy data products and ML models.

Lead a team of data engineers to design, build, and maintain scalable data pipelines, a Data Vault, data warehouses and marts. Own backlog, sprint planning, mentoring, stakeholder collaboration, data validation/monitoring, and platform improvements to deliver key metrics and data infrastructure across the business.

Design and build a single data platform and pipelines, ensure infrastructure and standards, provide governance and consultancy, and work with stakeholders to expand data capabilities across the insurance business.

Own and modernize the legacy SQL data estate: support and stabilize SQL Server databases, build and modernize ELT pipelines (including SAP integration), create analytics-ready data models, ensure HA/DR and recovery objectives, and operationalize data governance and privacy (GDPR/CCPA). Serve BI, reporting, and AI consumers while partnering with architecture, DBA, and GRC teams.

Build and maintain enterprise data platform and ETL/ELT pipelines, design and deploy data models, ensure data quality and governance, integrate third-party sources, and support analytics and AI initiatives across the organization.

Design, develop, and maintain enterprise ETL pipelines and scalable data processing using ADF, Informatica, Python, Spark/Databricks, and relational systems. Support Medicaid analytics, data validation, cloud migrations, production operations, and CI/CD practices.

Design, build, and maintain a petabyte-scale open-source data lakehouse and end-to-end data pipelines (ingest → transform → consume) ensuring performance, reliability, and data quality for analytics workloads.

Lead architecture and implementation of cloud-based data ingestion and ETL solutions. Build and maintain ETL pipelines, collaborate with stakeholders, create design documentation and tests, ensure governance and pipeline reliability, and coordinate with onshore/offshore engineering teams.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Lead development and operations of geospatial data pipelines and lakehouse systems. Migrate orchestration to Prefect, extend sources and transforms, optimize Postgres and Iceberg stores, build observability and AI-agent readiness, and integrate cross-team tooling with product and ML teams.