Top Data & Analytics Jobs

Leads Lexia’s business analytics and data strategy function, managing analysts and overseeing data engineering alignment. Owns BI strategy, Tableau governance, data governance, self-service dashboards, AI-enabled analytics, metric definitions, and data quality. Partners with Sales, Marketing, Customer Success, executives, and analytics engineering to translate business needs into insights, KPIs, models, and actionable recommendations. Manages analytics tools and vendors while ensuring responsible, secure, and effective use of enterprise data.

Develop and maintain customer audiences using SQL and PySpark in Databricks. Build and monitor data workflows feeding LiveRamp and other onboarding platforms, validate audience quality, analyze segmentation performance, and document reusable audience logic. Collaborate with data science, engineering, marketing, and strategy teams to operationalize model outputs and support CRM, media, and personalization activation.

Build and maintain Microsoft Fabric data pipelines, Lakehouse and Warehouse solutions, ingestion frameworks, Bronze/Silver/Gold data layers, transformations, dimensional models, and data-quality controls. Integrate ERP, CRM, manufacturing, construction, and other enterprise systems. Implement security, governance, DevOps, monitoring, documentation, and production support while partnering with architects, analysts, Power BI developers, and business stakeholders.

Executes ad effectiveness studies, analyzes media measurement data, and presents actionable insights to publishers and advertising platform clients. Manages 3–5 accounts, develops reports on advertising metrics, supports sales positioning, collaborates with analysts and product teams, and maintains client relationships while ensuring accurate, timely deliverables.

Design and implement data integration solutions, build data pipelines, manage cloud resources, and develop real-time data flows using AWS technologies and Snowflake.

Designs and maintains data architecture, ETL/ELT pipelines, data warehouses, lakes, and workflow automation. Ensures data quality, governance, security, availability, and performance while supporting reporting, analytics, dashboards, and machine learning. Translates business needs into technical data models, monitors data systems, and collaborates with Technology, Analytics, and business stakeholders. The role is based in the administrative office with work-from-home eligibility; travel is not required.

Leads enterprise data and actuarial technology strategy, modernization, architecture, delivery, operations, and production support. Owns cloud data platforms, actuarial applications, analytics, governance, resilience, controls, budgets, vendors, workforce planning, and regulatory readiness. Partners with actuarial, insurance, business, and technology leaders to execute multiyear roadmaps, modernize legacy systems, improve data quality and self-service analytics, and deliver measurable business value. Builds and leads a high-performing technology organization while overseeing risk, service health, disaster recovery, and operational performance.

Manage data domains and governance materials, including data dictionaries, metadata, taxonomies, schemas, and lineage. Apply standards that improve data quality and consistency across global markets. Partner with data, analytics, reporting, technology, stewardship, and investment teams to support reliable data deployment and usage. Explain data relationships and governance standards to stakeholders, support quality reviews, resolve inconsistencies, and improve documentation and workflows across media and marketing data systems.

Analyze complex data to support business decisions across marketing, finance, product, sales, medical affairs, and customer success. Responsibilities include defining data requirements, developing and automating data pipelines, building interactive dashboards, identifying trends and process improvements, and presenting insights to executives and external partners. The role requires advanced SQL, Python, BI, visualization, and cross-functional analytics experience, with an emphasis on using AI to improve efficiency.

Leads analytics for B2B sales, partnerships, revenue operations, and marketing teams. Responsibilities include forecasting, opportunity sizing, partner performance analysis, enterprise market intelligence, RFP analytics, lead scoring, funnel and lifecycle optimization, predictive modeling, experimentation, and executive insight generation. The role partners with Data Science and Engineering teams to improve revenue growth, onboarding, retention, campaign effectiveness, and sales performance across enterprise and SMB channels.