Top Data & Analytics Jobs

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.

Design, build, deploy, and support production data pipelines and enterprise data solutions in Databricks and GCP. Develop data models and layered architectures, ensure data quality, security, and governance, integrate multiple sources, and support analytics, AI/ML, reporting, and operational decision-making while collaborating with technical teams and federal stakeholders.

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 design, build, and maintain large-scale data pipelines and automation to provision data to consumers. Partner with data scientists and architects to deploy models, ensure performance and reliability of ETL, and champion agile best practices. Mentor engineers, vet big-data technologies, document evolving requirements, and collaborate cross-functionally to deliver secure cloud-native data solutions for a federal customer.

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.

Design, build, and support enterprise data platforms, semantic/ontology models, and scalable data pipelines. Implement data quality, lineage, metadata, CI/CD, governance (including RLS), and observability. Provide technical leadership, mentor team members, and collaborate with stakeholders to enable analytics, AI, and LLM-driven solutions.

Design and build advanced data visualizations for complex datasets within a Sales Analytics platform. Develop interactive, data-driven web applications (SPA) using D3.js, AngularJS, WebGL and modern front-end technologies to enable business decision-making.

The role involves building data platforms and pipelines, integrating AI solutions, and collaborating with product owners to address complex client issues.

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.

Design and maintain automated data workflows using Python and Databricks to ingest, transform, and catalog data via APIs; build authentication workbooks and Power Apps; communicate findings to technical and non-technical stakeholders and engage clients to gather requirements.

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.

Own and modernize the legacy SQL data estate: support and stabilize SQL Server databases, design HA/DR and recovery plans, build resilient ELT pipelines from sources including SAP, create analytics-ready data models for BI/AI, operationalize data governance and privacy (GDPR/CCPA), and mentor DBA succession while delivering documentation and runbooks.

Build and operate production data infrastructure: ingest, transform, and route large-scale streaming and batch data from heterogeneous enterprise sources; design ontology/semantic models and tuple/graph representations; ensure isolation, data quality, observability, and low-latency delivery for ML and product teams.

Design, build, and maintain data architectures, ETL pipelines, and data models to enable analytics across Marketing, Finance, Sales, and Logistics. Implement integrations from cloud sources, secure API connections, perform large-scale data mining and entity resolution, translate algorithms into code, and use SSIS and BI tools to deliver reliable reporting and dashboards.

Design, build and scale a 500+ TB Audience Management Big Data platform. Own data ingestion, validation, transformation and storage pipelines. Write server-side Spark/Scala code, perform R&D on new technologies, mentor engineers, and partner with product managers to meet roadmap and business needs.

Lead design and implement scalable data infrastructure and transformations. Build data repositories, convert data science algorithms into robust pipelines, mentor a small data engineering team, ensure delivery quality, and collaborate with Product, Data Analysts, and Data Scientists.

Design and implement scalable data infrastructure and production data transformations. Lead and mentor a small Data Engineering team, ensure quality delivery, collaborate with Product, Data Analysts, and Data Scientists to operationalize algorithms and build robust data repositories.