Top Data & Analytics Jobs

Maintains Tower business data accuracy, completeness, governance, and consistency across systems. Creates reports, dashboards, and analytical solutions; investigates anomalies, resolves data quality issues, supports data governance policies and standards, maintains data catalogs, performs audits, and identifies process improvements. Partners with Assets, Supply Chain, Finance, IT, and other stakeholders to provide trusted reporting and support business decision-making.

Serves as the bridge between procurement stakeholders and technical data teams, governing procurement data across ERP, master data, data lake, BI, and reporting platforms. Responsibilities include defining data standards, maintaining metadata and mappings, validating transformations, monitoring data quality, resolving data issues, supporting testing, and enabling analytics. The role also translates business requirements into technical specifications and contributes to procurement transformation and continuous improvement.

Deliver customer-facing technical training in data engineering and AI through virtual and onsite sessions. Create tailored courses, projects, certifications, and assessments aligned with customer roles, tools, and business outcomes. Partner with Customer Success, Sales, Product, and Content teams to design blended learning paths, drive platform adoption, and improve measurable skills. Support diverse technical and non-technical audiences across time zones, with occasional onsite travel and success metrics tied to customer satisfaction, bookings, and professional services revenue.

Leads the architecture, development, and support of enterprise data warehouses, lakehouses, dimensional models, ETL/ELT pipelines, and governed data products using Microsoft Fabric and related tools. Ensures data quality, security, scalability, performance, and reliability for analytics and reporting. Partners with technical and business stakeholders, documents data solutions, guides architecture decisions, mentors team members, and drives continuous improvement of data engineering standards and practices.

Supports enterprise master data applications by gathering and managing business, functional, and technical requirements across MDM, PIM, and DAM platforms. Leads stakeholder elicitation sessions, documents user stories, process flows, wireframes, acceptance criteria, and business rules, and partners with technical teams on solution decisions. Coordinates functional, integration, and user acceptance testing, including defect management, while aligning technical and non-technical stakeholders on priorities and approvals.

The Epic Business Intelligence Developer creates analytics content for Epic data, configures applications, writes SQL reports, and manages data models and analytics catalogs.

The role involves designing and managing data pipelines, developing canonical datasets, and ensuring data integration and compliance. Collaborate across teams to meet data needs and maintain data architecture.

Owns the data transformation layer powering agency reporting. Builds reliable analytical models, dimensional schemas, reporting architecture, monitoring, and alerting. Translates government and customer reporting requirements into reusable data models and tools, supports embedded analytics and recurring imports, and ensures authorization scoping prevents cross-organization data exposure. Partners across product and implementation teams while establishing data engineering practices and documentation.

Designs and maintains scalable ETL pipelines, data models, integrations, cloud databases, and healthcare data infrastructure. Ensures security, HIPAA/HITRUST compliance, reliability, monitoring, and documentation while collaborating with product, engineering, and clinical teams. Requires strong SQL and Python expertise, modern data warehousing and orchestration experience, cloud platform knowledge, and familiarity with healthcare interoperability and claims data.

Build and own commercial data models in Snowflake and dbt for ARR, bookings, pipeline, account scoring, ICP fit, upsell, and whitespace analysis. Establish semantic layers, governance, documentation, testing, monitoring, and CI/CD practices. Partner with Sales, Marketing, Finance, and enterprise data teams to deliver trusted self-service analytics, improve data infrastructure, and develop AI-powered automation. This senior individual contributor will own the roadmap and mentor analysts on data modeling and dbt.