Top Data & Analytics Jobs

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

Design, develop, and monitor large-scale cloud-native data solutions and ETL pipelines; support ML model deployment, ensure code hygiene and CI/CD, mentor engineers, produce documentation and runbooks, and engage with clients to deliver analytics-driven solutions.

Build and maintain data platform and production data warehouse; translate PM and quant requirements into technical solutions; design ETL, data modeling, cleansing, monitoring, and alerting systems; evaluate new tooling; optimize performance and manage vendor relationships.

The AWS Cloud Data Engineer will design, develop, and enhance AWS data environments, build scalable Data Lake solutions, and lead proofs-of-concepts into production.

Analyze Power BI reports, datasets, semantic models, and Alteryx workflows to trace data from source systems through ETL processes. Identify databases, schemas, tables, views, and columns; understand SQL transformations; map data relationships; and create source-to-target mappings and data lineage documentation. Collaborate with stakeholders to validate reporting logic, document architecture, and identify redundant or obsolete data sources.

Build and maintain AWS and Databricks Lakehouse data pipelines for healthcare real-world data and HEOR analytics. Develop data models, ensure governance and quality, optimize cloud performance and costs, and implement monitoring, observability, CI/CD, and orchestration practices. Collaborate with technical and business stakeholders, provide technical guidance, document solutions, deliver training, and report progress to senior leadership.

Develop and deploy scalable backend code, distributed systems, data pipelines, and reliable data infrastructure. Lead projects from design through production, improve platform scalability and performance, and collaborate with cross-functional engineering teams. The role requires strong programming, advanced SQL and data modeling, cloud platform experience, and infrastructure-as-code skills. Bonus experience includes large-scale processing frameworks and operating critical production systems with four-nines availability.

Design, build, monitor, and optimize scalable ETL/data pipelines on Azure. Collaborate with scientists and engineers to integrate systems, support analytics/ML, and ensure performance, cost-effectiveness, and data governance.

The Data Engineer will design and build solutions for investment insights using big data technologies, developing data pipelines, and collaborating with cross-functional teams.

Design and maintain ingestion pipelines for 835/837 and scanned EOBs: OCR, LLM-assisted extraction, integrations (SFTP, clearinghouses), data modeling, validation, and reconciliation to produce deterministic, trustworthy healthcare records.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Lead architecture and implementation of an AWS-native data platform powering analytics, ML, and AI workflows. Build scalable ETL/ELT pipelines (Airflow, Fivetran, dbt), enforce governance, observability (Datadog), and data quality, and provide technical leadership across Data Science, Analytics, Product, and Engineering.

As a Staff Data Engineer, you'll oversee Gamma's data infrastructure, architect scalable systems, mentor engineers, and drive strategic decisions for data usage across the organization.

Lead and mentor multiple Databricks-focused data engineering teams, define engineering best practices and reusable frameworks, drive CI/CD and performance/cost optimization, build and support analytical and streaming pipelines, and collaborate with architects, product owners, and cross-functional partners to deliver high-quality data products.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from varied sources. Work with event-driven and batch architectures, implement authentication and integration patterns, ensure observability/monitoring, optimize data flows for cost and performance, and collaborate with product, infrastructure, and engineering teams to onboard and maintain data sources at scale.

Lead architecture and implementation of fleet telemetry, time-series storage, streaming pipelines, and CRM/warehouse integrations on AWS. Build tools for proactive fleet health, partner cross-functionally (firmware, manufacturing, ops), support on-call as systems mature, and design LLM-driven automations to increase team leverage.

Design and implement scalable data infrastructure and pipelines on AWS using Spark and Scala. Build fault-tolerant batch and streaming workflows, optimize storage formats and engines, enforce automated data quality checks, and collaborate with Data Science and Product teams to deliver reliable data assets and APIs.

Lead design and implementation of identity resolution and data governance systems. Build batch and streaming pipelines, APIs, and services to ingest, normalize, link, and version identity data. Ensure auditable matching, data lineage, quality checks, schema enforcement, access controls, and privacy-by-design. Collaborate cross-functionally with product, analytics, legal, privacy, and security teams and create documentation, monitoring, and runbooks.

Lead design and operation of identity resolution and data governance systems. Build batch and streaming pipelines, APIs, and tooling for identity ingestion, matching, lineage, quality, access controls, and privacy compliance (GDPR/CCPA). Collaborate with product, analytics, legal, and security to ensure privacy-by-design, monitoring, and documented runbooks.

Lead the BI function owning Azure-based BI infrastructure and data pipelines, write and optimize complex SQL, maintain Power BI reporting and revenue analytics, lead and mentor BI analysts, collaborate with Sales/Finance/Operations, and ensure data quality and documentation standards.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure, implementing data governance, and collaborating with teams to meet data needs.

Lead design and implementation of a secure, decentralized data sharing framework and resilient patterns (e.g., CQRS) for distributed microservices. Align storage strategies across relational, indexing, and time-series engines, maintain multi-domain data models and contracts, collaborate with backend, infrastructure, and platform teams to scale and secure data systems across bare-metal and cloud environments.

Design, develop, and maintain databases and scalable data pipelines. Build stored procedures, triggers, and ETL processes; perform testing, tuning, security, and data quality work. Collaborate with developers and stakeholders, document architecture, review code, and support reporting and integrations to enable restaurant and customer-facing systems.

Build, operate, and monitor ETL/ELT pipelines between marketing, sales, and operational systems into Databricks. Implement dimensional models, identity resolution, data quality checks, API integrations, and scheduled/event-driven syncs to support segmentation, attribution, and campaign activation. Maintain documentation, alerting, and runbooks.