Top Hybrid Data & Analytics Jobs

Analyze sales, marketing, and customer success performance, including pipeline, funnel conversion, campaigns, retention, and revenue drivers. Build and maintain dbt models and marts using Salesforce and modern data-stack tools, while owning data quality and documentation. Contribute to Lighthouse, a BI-as-code platform, and develop AI-assisted analytics workflows. Partner with stakeholders to frame ambiguous questions, deliver recommendations, and communicate progress in two-week sprints.

Lead IEX's Market Data, Access & Member Services team, focusing on driving revenue growth, strategic market data initiatives, and enhancing client services.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

As an SEO Analyst, you will analyze data to improve SEO strategies, provide performance reports, and collaborate with teams to enhance user experiences and brand visibility.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Build and maintain ELT pipelines, data models, semantic layers, and personalization signals for client retention and marketing tests. Partner with machine learning, product, marketing, design, and architecture teams to prepare predictive data, connect signals to CRM channels, support real-time event processing, and document a scalable data foundation. Use AI coding assistants daily to accelerate development, testing, and reviews in a fast-moving discovery environment.

Designs scalable data models, governance processes, quality checks, documentation, and internal data tools. Partners with Data Engineering and business stakeholders to create trustworthy, discoverable, AI-ready data assets supporting analytics, reporting, downstream applications, and machine learning initiatives. Uses AI tools to improve modeling, governance, analysis, documentation, and automation workflows.

Build and lead the data engineering ecosystem supporting analytics, BI, and data science. Design scalable pipelines, cloud infrastructure, Snowflake data warehouses, governance controls, validation frameworks, and machine learning workflows. Collaborate with engineering, data science, product, and business stakeholders on architecture and technology decisions. Develop AI agents and secure enterprise AI integrations, participate in on-call and incident management, document standards, and mentor data engineers.

The Platform Intelligence Engineer designs and maintains data infrastructure to support decision-making, analyzes data for performance metrics, and collaborates with stakeholders to operationalize insights.

Lead and grow Analytics Engineering: define data warehouse and BI strategy, build and optimize ETL/ELT pipelines, design dimensional models in BigQuery using dbt and Airflow, own BI tooling (Looker), ensure data quality and governance, and mentor/manage a distributed analytics engineering team while partnering with stakeholders and platform engineers.