Top Hybrid Data & Analytics Jobs

Designs and builds modern data applications, pipelines, transformations, enrichment processes, provisioning layers, and interfaces supporting clinical and operational healthcare processes. Analyzes clinical data to identify trends, collaborates with Product, Platform, and Architecture teams, mentors junior data engineers, and evaluates emerging technologies. The role emphasizes cloud computing, big data, data science, modern development frameworks, source control, documentation, and repeatable engineering practices.

Manages analytics projects, coordinates subteams and offshore resources, reviews data analysis and project outputs, and presents recommendations to clients and management. Performs data extraction, ETL, warehousing, statistical analysis, machine learning, visualization, model development, validation, and performance tuning using SAS, R, SQL, and related tools.

Analyze programmatic advertising marketplace trends, buyer behavior, auction dynamics, publisher performance, and revenue data. Build scalable reports, dashboards, and KPI frameworks; investigate performance fluctuations; identify monetization and optimization opportunities; and translate complex analyses into actionable recommendations. Partner with cross-functional teams, support strategic planning, present insights to executive stakeholders, and improve analytics workflows using SQL, automation, and business intelligence tools.

Supports research and analytics on economic, real estate, and financial trends. Responsibilities include organizing and evaluating data, developing quantitative and qualitative insights, maintaining internal databases, preparing market analyses, and creating content for presentations, webinars, podcasts, websites, and other communications.

Leads architecture, optimization, scaling, and ownership of Zoox’s enterprise and AI data platform. Builds self-healing pipelines, governance frameworks, data quality and observability systems, secure access controls, lifecycle policies, and automated CI/CD deployment infrastructure. Optimizes Databricks and AWS EMR performance and costs while enabling low-latency, AI-ready data foundations. The role also drives enterprise security, compliance, schema evolution, and developer velocity, with opportunities to apply GenAI, Terraform, and EKS.

Designs, builds, optimizes, and supports enterprise-scale batch and streaming data pipelines and lakehouse solutions. The role uses Apache Spark and Microsoft Fabric to deliver analytics-ready data for reporting, AI/ML, and decision-making. Responsibilities include ETL/ELT development, data modeling, governance, quality, security, CI/CD, observability, production support, architecture reviews, and technical documentation. The senior engineer also mentors colleagues, drives engineering standards, and collaborates with analytics, platform, security, and business teams.

Leads the enterprise data governance program by establishing governance councils, operating models, data ownership and stewardship frameworks, lifecycle controls, KPIs, scorecards, and maturity assessments. Partners with IT, Legal, Compliance, Privacy, and business stakeholders to operationalize auditable data controls, enforce governance standards, and deliver executive reporting across the organization.

Designs, builds, and operates scalable batch and real-time data infrastructure processing trillions of data points. Responsibilities include platform architecture, infrastructure as code, self-healing systems, observability, developer tooling, pipeline optimization, security controls, database tuning, and distributed-system troubleshooting. The role collaborates with engineering and product teams, improves reliability and performance, and enables safe access for data engineers, software engineers, and AI agents.

10-week summer internship working on data analytics, reporting, business analysis, process and continuous improvement, and project management projects. Includes mentoring, training, and exposure to analytics tools and enterprise technology teams.

Builds and maintains scalable batch and streaming data pipelines supporting analytics, machine learning, and business operations. Responsibilities include writing Python, SQL, and Spark code; ensuring data quality and reliability; monitoring and troubleshooting pipelines; implementing testing and observability practices; documenting data architecture; and collaborating with analysts, data scientists, product managers, and engineering teams in an agile environment.