Top Senior Data & Analytics Jobs in Kansas City, MO

Manage client data, analytics, and AI managed-services workstreams. Develop data pipelines, dashboards, reporting, and analytics outputs; remediate data-quality issues; tune AI models and platform components; and improve data operations, observability, and reporting workflows. Collaborate with clients to translate business needs into data solutions, interpret insights, review deliverables, and mentor junior team members.

Leads client managed services engagements involving data, analytics, and AI operations. Oversees project planning, budgets, timelines, data quality, master data management, pipelines, platform maintenance, model tuning, and performance monitoring. Translates business requirements into data solutions, reviews deliverables, manages risks, and coaches team members while promoting AI-enabled automation and adherence to professional standards.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Develop and modernize enterprise reporting, analytics, data models, pipelines, dashboards, and cloud data solutions across banking organizations. Partner with business and technology stakeholders to translate requirements into scalable analytics solutions using Salesforce, Tableau, Snowflake, Databricks, SQL, and Python. Support data migration, governance, quality, security, AI-enabled analytics, and executive reporting while delivering actionable insights and measurable business outcomes.

Supervises Media Intelligence staff while producing client reports, QBRs, and ad-hoc analyses. Presents measurement findings, oversees workflow and deliverable quality, ensures data governance and privacy compliance, mentors analysts, and supports BI tool evaluation and process improvement. Requires advanced analytics, digital media measurement, BI platform, data visualization, and people-management expertise.

Designs and maintains cloud data pipelines, data platforms, ETL and real-time streaming architectures. Builds PySpark jobs, AWS Lambda workflows, EventBridge orchestration, Aurora PostgreSQL schemas, Kafka producers and consumers, and Snowflake solutions. Responsibilities include data governance, lifecycle management, replication, query optimization, reporting, anomaly analysis, standards development, and mentoring team members. The role modernizes legacy systems and supports enterprise analytics initiatives.

Own the organization’s data platform, including production pipelines, cloud warehouse architecture, dimensional data models, data quality, testing, documentation, and lineage. Partner with Finance, HR, and Operations stakeholders to build reliable reporting solutions. The role requires independent technical ownership, performance and cost optimization, schema management, and scalable integration of new data sources and business systems.

Supports enterprise data governance for acquired-company integrations, including structured and unstructured data inventories, classification, ownership, retention, legal holds, quality, metadata, lineage, stewardship, and compliance documentation. The role partners with Legal, Privacy, Risk, Security, technology teams, and vendors to support migration, archival, modernization, analytics enablement, reporting, readiness metrics, and defensible disposal activities.

Supports data governance for acquired-company integration and transformation initiatives. Responsibilities include inventorying structured and unstructured data, documenting ownership, classification, retention, legal holds, privacy, quality, metadata, lineage, and stewardship requirements. The role maintains catalogs and glossaries, supports source-to-target mapping and reconciliation, prepares governance metrics and dashboards, and coordinates with business, technology, legal, privacy, risk, security, and implementation teams to ensure data is trustworthy, compliant, and ready for migration, archival, modernization, and analytics.

Leads client experimentation and personalization programs across web, mobile, and media channels. Designs testing roadmaps, hypotheses, KPIs, audiences, and statistical parameters; oversees test execution, quality assurance, monitoring, and analysis. Translates results into executive recommendations, ROI projections, and strategic action plans. Advises clients on experimentation maturity, governance, prioritization, and best practices while training internal and client teams. Manages stakeholder relationships, concurrent roadmaps, and opportunities for account growth.

Design, build, and optimize modern Azure and Databricks data platforms for clients. Develop batch and streaming ingestion pipelines, lakehouse and medallion architectures, dimensional models, and transformation workflows. Lead discovery sessions, requirements workshops, architecture discussions, and solution reviews. Recommend practices for governance, lineage, monitoring, DevOps, security, and data modeling while translating business needs into scalable technical solutions and communicating with technical and executive stakeholders.