Top Hybrid Data & Analytics Jobs

Lead data engineering initiatives supporting analytics, machine learning, and business intelligence teams. Design complex data warehouse and data lake models, manage large-scale SQL and Python ETL/ELT pipelines, maintain data quality and lifecycle standards, and translate strategic priorities into actionable projects. Provide technical leadership, mentorship, architectural guidance, project planning, and cross-functional collaboration with product, infrastructure, and architecture teams.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Analyze payment fraud data using SQL and dashboards, identify anomalous behavior and risk trends, evaluate fraud controls, and recommend improvements. Present findings to leadership and collaborate with Product, Data Science, Engineering, Customer Experience, and Risk Analytics teams. The intern will also document risk processes and responsibly use generative AI to improve workflow efficiency and quality.

The Economist will analyze travel and spending data to identify trends, craft insights, and present findings, serving as an economic advisor to leadership.

Leads business and system requirements gathering, stakeholder solutioning, documentation, configuration, troubleshooting, quality assurance, testing, and release-note coordination. The role supports customization requests, reviews configurations, oversees project testing, monitors junior business analysts, and helps deliver solutions for fintech and financial-services clients.

Own and maintain dbt data models across design, testing, deployment, monitoring, and documentation. Translate ambiguous Marketing and Product needs into reliable data solutions, write design documents, support production issues, create runbooks and schema documentation, and coach junior engineers. The role uses SQL, Python, Snowflake, dbt, Dagster, and AWS while partnering closely with Marketing, Data, and MarTech teams.

Develop and advance the enterprise data platform by gathering business requirements, designing data models and warehouses, and building data visualization solutions. Administer reporting, analytics, and visualization platforms while promoting best practices. Collaborate cross-functionally, apply business and process knowledge, manage multiple projects, communicate strategically, and provide technical leadership in data engineering and business intelligence.

Design and administer enterprise dashboards and reporting solutions (Tableau, Power BI, Microsoft Fabric). Translate complex business questions into compelling data stories, partner with stakeholders and BI engineers, champion visualization standards and governance, mentor analysts, and improve reporting usability and trust across the organization.

Design and maintain billing data systems that reconcile patient engagement data, codify auditable billing logic in BigQuery SQL, automate data cleanup with Python or Google Sheets scripting, and generate accurate, version-controlled invoice files. Investigate mismatches and edge cases, document workflows, and collaborate with Finance, Sales, Engineering, and Product to improve billing inputs and processes.

Lead and grow a data engineering team responsible for allocating cloud costs to products and customers. Own reproducible, versioned metric publication for Finance, platform-team cost contracts, coverage onboarding, identity mapping, and cost-to-revenue joins. Drive roadmap delivery across teams, improve cost accuracy and coverage, reduce key-person risk, and use AI tooling to increase engineering productivity.