Top Senior Data Engineer Jobs

Lead design, maintenance, and optimization of on-prem and cloud data platforms. Perform DBA duties for SQL Server, manage HA/DR and replication, troubleshoot performance, lead on-prem to Azure migrations, maintain Azure data services and Microsoft Fabric, optimize cloud cost/performance, implement monitoring, collaborate with architects and BI teams, and mentor junior engineers.

Lead and mentor a distributed data engineering team while architecting and building a next-generation, largely open-source data platform. Own hands-on POCs, pipeline design, schema and query optimization, ETL reliability, observability, testing, and CI/CD. Evaluate and recommend technologies, drive migration from vendor lock-in, and translate advertising and market research needs into robust technical solutions.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

Design, build, and maintain large-scale data pipelines and ETL processes (NiFi, Kafka, AWS) using Python and Java. Create data models, automated cleaning, monitoring, unit tests, and warehousing solutions. Collaborate with engineering teams, use Docker and JIRA, and support analytics and data science consumers.

Build and improve engineering platforms, pipelines, and developer tooling to raise developer productivity and reliability. Contribute to AI-assisted workflows, instrument engineering health signals, partner on shared tools and governance, and apply secure development practices across Microsoft Security.

Design, build, and optimize scalable data platforms, pipelines, and lakehouse/warehouse solutions using Microsoft Fabric and Azure. Implement ETL/ELT, data migration from SQL Server, governance, CI/CD, streaming integrations, and Power BI connectivity to enable analytics and AI initiatives while ensuring security and performance.

Design and build scalable data lakes, warehouses, and lakehouses. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest data from third-party APIs, optimize columnar file formats and SQL, support ML initiatives, and consult with stakeholders to translate business needs into data architecture roadmaps.

Design and build production-grade data pipelines on Databricks and AWS extracting from enterprise ERPs, implement medallion architectures, ensure data quality/governance, collaborate with business SMEs, and deliver AI-ready data products.

Own day-to-day Snowflake administration and the Sigma semantic layer; manage ingestion (Fivetran), dbt modeling scope, reverse-ETL to Salesforce/HubSpot/NetSuite, data quality SLAs, reporting and dashboards, partner with managed data vendors, design automation and reproducible month-end financial analytics, and drive analysis and experiments for AdOps, Sales, and Finance.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Lead technical design and implementation of production-grade data platforms, ETL/ELT and streaming pipelines, containerized Python data applications, Snowflake and dbt modeling, observability, CI/CD, and AI/ML data integrations to enable scalable data products and business-aligned analytics.

Own and scale Provable's data platform on GCP: build and optimize ingestion pipelines from blockchain indexers, design privacy-aware data models, manage Postgres and BigQuery, develop high-performance data-serving APIs, ensure data quality and observability, and use IaC (Terraform, Helm) and Kubernetes to deploy and cost-optimize environments.

Design, develop, test, and maintain ETL solutions and data warehouse structures using SSIS and SQL Server. Build and support SSAS models, optimize ETL performance, ensure data quality, partner with stakeholders, and produce technical documentation to support BI and reporting initiatives.

Design, build, and maintain Snowflake pipelines using Python and SQL to produce identity assets. Investigate record-level PII data quality, enhance AI-assisted and rule-based matching engines, build evaluation and monitoring tooling, collaborate with Data Science and Product, and support CI/CD, QA, and on-call responsibilities for production identity pipelines.

Own and scale streaming Kafka pipelines and large-scale Spark batch jobs processing billions of rows. Drive pipeline reliability, observability, data modeling, and cost/latency optimizations. Partner with data scientists and platform engineers, define SLOs, improve efficiency, contribute to shared infrastructure (Airflow, libraries, data catalog), and mentor junior engineers.

Design and build scalable cloud-based Azure data solutions, implement ETL and integration, ensure data quality and interoperability, support reporting and analytics, mentor junior engineers, and follow Agile/DevOps practices to deliver production-ready solutions for healthcare use cases.

Lead architecture and execution of Faire's data platform and analytics capabilities. Build and operate scalable, secure ETL/ELT, streaming, storage, compute, orchestration, and data warehousing at petabyte scale. Drive performance, cost, monitoring, governance, and self-serve analytics; influence cross-functional teams and mentor engineers.

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Design, build, and maintain scalable batch and streaming data pipelines and storage on AWS. Own features end-to-end, collaborate with cross-functional teams, ensure data quality and observability, mentor engineers, and occasionally support field deployments.

Design, build, and maintain ELT and streaming data pipelines; operate Dagster/Airflow and dbt workflows; own Snowflake schemas and data quality; resolve data incidents; contribute to architecture, documentation, analytics tooling (Hex, AI agents), and participate in on-call rotation.

Design, build, and maintain scalable analytics pipelines and data models; ensure data quality, reliability, and security; enable self-service data access; implement monitoring and logging; collaborate with analysts, data scientists, and stakeholders; evaluate and manage data tools and vendors; apply AI tools to improve development and data reliability.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest from third-party APIs, optimize columnar file formats, work with Snowflake, Spark, and AWS, and advise stakeholders on data architecture roadmaps.

Design and build end-to-end data pipelines and analytics in Microsoft Fabric, develop Power BI semantic models and dashboards, ensure data governance and quality, enable business stakeholders, and optimize performance and adoption across merchandising, supply chain, stores, and omnichannel.

Design, build, and maintain scalable data pipelines and integrations across AWS, Azure, and BigQuery. Normalize, aggregate, and ensure data quality for large web and transactional datasets, leveraging APIs, ETL/orchestration tools, and big-data technologies to support analytics and BI in an e-commerce environment.

Designs, builds, and supports scalable data platforms, ETL, APIs, and data solutions (on-prem and cloud). Collaborates with architecture, security, and LOBs; builds CI/CD pipelines; troubleshoots production incidents; mentors team members; and evaluates tools and technical strategies to enable analytics, data warehousing, streaming, and machine learning use cases.