Top Data & Analytics Jobs

Own marketing channel performance tracking, reporting, experimentation, dashboards, forecasting, and LTV analysis. Develop measurement frameworks, identify data issues, and translate complex findings into actionable recommendations for Marketing, Growth, Product, and Finance stakeholders. The role requires strong SQL, BI dashboarding, marketing measurement, experimentation knowledge, and effective analytical communication, with AI tools used to accelerate reporting and analysis.

Develop and manage Workday PSA and Financials data solutions, including conversions, transformations, integrations, reporting, APIs, EIBs, and ETL processes. Create technical documentation, perform data analysis and loading, support migrations and M&A initiatives, and ensure security, compliance, access controls, and data quality. Partner with business analysts, functional leads, engineers, and security teams while managing multiple projects and serving as a Workday data subject matter expert.

Build and maintain a production data platform integrating RMS, Azure SQL, HubSpot, and vendor APIs. Develop pipelines, data models, quality gates, APIs, system-of-record write-backs, performance improvements, and explainable lineage. Build production AI-agent workflows with evaluation, guardrails, and monitoring. Own features end to end, contribute to applications, support incident response, and collaborate with product managers and traders.

Technical lead for Finance Technology data engineering: design and build scalable batch and real-time data pipelines (Hadoop/Spark/PySpark/Scala/Java), optimize data models and APIs, drive cloud-based data architecture (AWS/GCP/Azure), enforce governance/security, and mentor engineers to deliver enterprise financial data services.

Designs and owns data architecture, models, governance, security, and scalable solutions on modern data platforms. Translates business needs into technical designs, guides data engineers, enables AI use cases, leads stakeholder discussions, and identifies opportunities to expand the client engagement. The role is client-facing and requires local Indianapolis availability for onsite or hybrid work.

Build and maintain production knowledge graphs in Stardog using semantic web standards, ontology modeling, SPARQL, virtual mappings, Python, and SQL. Integrate life science data sources, implement data quality and validation, automate CI/CD deployments, support governance and lineage, and collaborate with subject matter experts to develop defensible semantic models.

Leads the design, development, and operation of enterprise data integration solutions, including cloud data lakes, warehouses, ETL pipelines, APIs, data models, and automated CI/CD workflows. Partners with global business and technology stakeholders to deliver scalable, secure, governed, and high-quality data systems. Oversees pipeline operations, performance optimization, testing, documentation, data cataloging, and integration developer standards while supporting project and business-as-usual initiatives.

Conduct real estate market research across asset types, maintain data integrity, analyze market trends, and develop reports, presentations, pitches, and white papers. The role creates narratives around market participants and demand drivers, evaluates research resources, supports national and global initiatives, trains brokers on tools, and mentors junior staff. Success requires strong analytical, writing, communication, data management, Excel, and PowerPoint skills.

Provide senior-level data engineering for analytics: resolve complex technical issues, build decision-support reports and dashboards, lead projects, prepare and integrate data, coach staff, and ensure high-quality analytics to support academic, research, and healthcare goals.

Develops and maintains dbt models in Snowflake, transforms source data into governed analytical datasets, and designs dimensional data models. Builds Gold-layer data products supporting Power BI, reporting, analytics, and approved AI use cases. Implements data quality tests, documentation, lineage, source control, CI/CD, and production monitoring. Partners with business stakeholders, analysts, and Data Engineering to define metrics, troubleshoot data issues, validate releases, and support certified semantic datasets.