Top Senior Data & Analytics Jobs

Translate business needs into technical and analytical specifications; gather requirements; build dashboards, forecasts, and financial/productivity analytics; manage QA, testing and release; document processes; train users; provide post-implementation support; and serve as liaison between business, IT, and operations.

The Senior Data Analyst will analyze sales and marketing funnel performance, customer acquisition, conversion, segmentation, forecasting, experimentation, and market expansion. They will integrate multiple data sources, identify data quality and attribution issues, create scalable datasets and dashboards, monitor performance, and deliver executive-ready recommendations. The role partners cross-functionally with Sales, Marketing, Product, Finance, Data Science, and Data Engineering.

Leads cross-channel measurement strategy and analysis of digital media, web, and business performance. Builds Tableau dashboards and data pipelines, evaluates KPIs, ROI, customer acquisition costs, and lifetime value, and translates findings into actionable insights. Presents analyses through dashboards, deep dives, and infographics while collaborating with agency teams and clients.

Leads investment data operations, governance, production support, and business analysis. Manages Investment Data Stewards and Business Analysts, oversees data quality and controls, resolves production issues, translates stakeholder needs into requirements, supports technology implementations and UAT, and coordinates with Investments, IT, vendors, and enterprise partners. Promotes accurate, timely, and effectively governed investment data across systems, reporting platforms, and operational processes.

Leads Scout’s geospatial, data analytics, enterprise GIS, data governance, automation, and AI capabilities supporting renewable energy development. Establishes strategy, architecture, standards, and scalable systems for site selection, project development, engineering, construction, and investment decisions. Builds and manages a technical team, oversees technology investments and vendors, integrates business platforms, drives digital transformation, and partners with executive and cross-functional leaders to deliver measurable business outcomes.

Own data engineering delivery for multiple clients, including designing and maintaining SQL and Python ETL/ELT pipelines, integrations, data warehouses, and Airflow orchestration. Translate business requirements into scalable data solutions, manage scope and priorities, troubleshoot data integrity and performance issues, and establish quality controls and documentation. Serve as a client-facing technical lead, guide architecture through influence, and use AI-assisted coding tools while supporting team best practices.

Administer and optimize Microsoft SQL Server and Oracle databases in a federal enterprise environment. Responsibilities include installation, patching, monitoring, backup and recovery, performance tuning, access management, security auditing, cloud migration support, SQL development, documentation, ServiceNow incident response, root cause analysis, and on-call support. The role requires at least four years of database administration experience and the ability to obtain Public Trust clearance.

Own the end-to-end architecture and roadmap for MyFitnessPal’s product data stack. Coordinate data engineering, business intelligence, product analytics, and backend teams; establish data movement and standardization practices; lead tool evaluations and platform migrations; implement governance, PII handling, deletion workflows, access controls, and cost optimization; and document architecture standards across the organization.

Designs and maintains automated risk-reporting solutions, integrating Python, Shell, SQL, AI workflows, Power BI, and dimensional data models. Modernizes SAS-based processes into scalable Python and Anaconda pipelines, develops data marts and star schemas, tunes queries, and supports risk-system production operations, governance, controls, monitoring, and change management. Partners with reporting teams and stakeholders to improve data accuracy, reliability, visualization, and operational effectiveness.

Lead data engineering for client reporting, measurement, internal operations, and AI-driven initiatives. Build and maintain Snowflake pipelines and dbt models, integrate marketing and business data, troubleshoot issues, and maintain governance, privacy, and consent controls. Develop semantic layers and governed access for AI tools. Shape architecture and technical roadmaps, translate business needs into data solutions, and build and lead a data support team.