Top Hybrid Data & Analytics Jobs

Designs, develops, tests, deploys, and monitors scalable data pipelines using Azure Data Factory, Databricks, Python, SQL, and dbt. Integrates source systems with data warehouses and other platforms, automates data processes, improves infrastructure scalability, and supports data migrations during mergers and acquisitions. Ensures data security, privacy, retention, and effective enterprise data management.

Design, build, and administer CMAP’s enterprise GIS platform, data pipelines, APIs, and analytical frameworks; set standards, deliver training, create self-service spatial tools, and collaborate with stakeholders to integrate spatial analysis into planning workflows.

Supports healthcare survey data operations through data collection, validation, quality review, file preparation, issue resolution, and documentation. Reviews data for completeness, consistency, outliers, and accuracy; communicates with respondents to clarify information; supports reports, statistical tables, custom analyses, and cross-functional data needs. Requires strong Excel skills, attention to detail, communication, and the ability to work with survey data and databases. The role is hybrid with on-site work in Englewood, Colorado and regular conference travel.

The Data Architect will define enterprise data architecture standards and design scalable, secure Azure-based data platforms. Responsibilities include data modeling, semantic layer design, governance, metadata and security architecture, AI-ready data foundations, platform modernization, vendor and business engagement, architecture documentation, and operational processes. The role also guides CI/CD, Infrastructure as Code, automation, prototyping, and engineering best practices while influencing enterprise-wide transformation initiatives.

Own end-to-end market research engagements, including research design, discussion guides, qualitative and quantitative analysis, insight synthesis, presentations, and strategic recommendations. Partner with enterprise insights, marketing, and product leaders, leverage AI-powered research tools, build customer relationships, develop scalable methodologies, and collaborate with Sales, Customer Success, and Product to shape Strella’s research offering.

Develops and supports ETL workflows, REST APIs, backend services, AWS file-transfer automation, legacy applications, and data-processing systems. Troubleshoots production issues, manages deployments across environments, improves monitoring and recovery, and documents technical systems. Requires SQL, relational database, API, data integration, batch-processing, and production support experience, along with U.S. citizenship and the ability to pass a Public Trust investigation.

Develop and maintain customer audiences using SQL and PySpark in Databricks. Build and monitor data workflows feeding LiveRamp and other onboarding platforms, validate audience quality, analyze segmentation performance, and document reusable audience logic. Collaborate with data science, engineering, marketing, and strategy teams to operationalize model outputs and support CRM, media, and personalization activation.

Build and maintain Python-focused data analytics applications, pipelines, integrations, batch workflows, and data-quality solutions for Prime Services financial data. Partner with quantitative analysts and business stakeholders to translate requirements into production-grade systems. Support global production operations, monitoring, incident resolution, and data governance while using technologies such as Kafka, Spark, S3, Trino, and Starburst. Contribute to AI-powered monitoring agents and scalable analytics platforms.

Build and maintain production-grade data models while migrating workflows from Alteryx to dbt. Manage Dagster or Airflow pipelines in Redshift, create Tableau dashboards, and translate technical data into RevOps insights. Partner with Sales, Marketing, and Customer Experience leaders on KPI reporting, ABM, and customer health scoring. Explore AI, LLM, predictive analytics, and SageMaker machine learning applications including churn prediction and lead scoring.

Lead the architecture and evolution of enterprise-scale data platforms, applying distributed-systems and advanced data-modeling patterns. Establish technical standards across dbt, Airflow, Snowflake, Kafka, ETL, cloud infrastructure, security, monitoring, CI/CD, testing, and source control. Solve complex data problems, partner with senior leaders, provide technical governance, mentor engineers, and drive platform and process improvements.