Top Hybrid Data & Analytics Jobs

Design, build, and maintain scalable ETL pipelines and data integration solutions using SQL Server, SSIS, and Azure Data Factory. Lead logical and physical data modeling, create data marts and operational stores, ensure data quality and governance, optimize ETL performance, and collaborate with stakeholders to translate business needs into enterprise data solutions.

Design and deliver cloud-based data architectures, ETL/ELT pipelines, and data lake/warehouse solutions for a Department of War client. Lead a small engineering team, implement migration strategy, build live-data dashboards, recommend tools, and coordinate with government PM. Hybrid role at Fort Bragg with quarterly travel; requires active Secret clearance and US citizenship.

Build, maintain, and troubleshoot data pipelines moving data into Snowflake. Write dbt models with tests/docs, backfill and validate data, investigate upstream data issues, run incident response for MSSQL/Snowflake/S3, and document processes.

The Locality Expert will conduct market research, collaborate on policy creation, perform quality checks, and provide local context for data accuracy in the automotive industry.

Technical staff-level data engineer who sets direction for ingestion, storage, and search systems. Designs scalable, reliable pipelines and storage, establishes architecture and observability standards, mentors engineers, leads complex multi-quarter initiatives, participates in on-call, and partners with AI/ML and product teams to deliver queryable, high-volume telemetry for analytics and detection.

Lead and define Canary's analytics function: own analytics strategy, platform architecture, data pipelines, dashboards, KPIs, and governance. Enable self-serve analytics, deliver insights to leadership and cross-functional teams, and work directly with enterprise customers on reporting and integrations.

Design, build, test, and maintain batch and streaming data pipelines; ingest data from varied sources; write SQL and Python transformations; monitor data quality and observability; support analytics, reporting, and data science; participate in code reviews, troubleshooting, and production support; collaborate with stakeholders to deliver scalable data solutions.

Own the 2-3 year architecture and reliability of high-volume catalog, pricing, and inventory ingestion. Lead migrations from legacy batch to modern systems, define data SLAs, build observability and orchestration, mentor senior engineers, and resolve cross-team, high-severity data problems.

Owner of enterprise data health: monitor and enforce data quality across CRM, AMS, ERP, data warehouse and integrations. Maintain system-of-record registry, track ownership gaps, validate integration fidelity, support SDLC reviews, produce data health scorecards and executive reports, and drive issue resolution through Service Desk with stakeholders and technical teams.

Lead architecture and evolution of Cardlytics' AWS-based data compute platform and lakehouse. Define standards for batch, streaming, ML, and AI workloads; build semantic layer, feature store, and governance. Partner with analysts and engineers, drive CI/CD (Terraform, GitHub Actions), monitoring, security, cost governance, and incident response while mentoring teams and shaping platform strategy.