Top Data & Analytics Jobs

Leads knowledge engineering and content operations across legal and consulting practices. Partners with subject-matter experts to structure expertise into taxonomies, decision trees, playbooks, metadata standards, workflows, and AI-ready content. Governs content quality, enterprise search, retrieval systems, and experience-management data models while supporting automation, GenAI, ethical-wall compliance, and institutional knowledge reuse. Measures findability, content health, and reuse through reporting dashboards.

Own Power BI migration and analytics platform governance while leading data initiatives, reporting automation, data quality validation, and enterprise data warehouse scalability. Partner with clinical, operations, finance, and leadership stakeholders to translate business needs into analytical requirements, define metrics and acceptance criteria, troubleshoot integrations, and develop reliable internal and client-facing reporting solutions.

The Senior Business Analyst will identify and implement AI-driven process improvements, build AI-powered workflows and automations, and collaborate with engineering on LLM-based document review and data extraction. Responsibilities include integrating internal systems, developing prompts, creating reusable tools, building dashboards and operational reports, querying Redshift with SQL, monitoring automation performance, and recommending emerging AI solutions to improve efficiency and decision-making.

Leads Data Analyst Assistants and supports assessment, evaluation, and research projects. Responsibilities include data collection, cleansing, transformation, modeling, analysis, dashboard development, visualization, stakeholder coordination, workflow improvement, and analytical tool training. The role provides project guidance, ensures timely deliverables, safeguards sensitive data, and communicates insights through compelling data storytelling.

Build and operate data ingestion, transformation, orchestration, and quality pipelines; design dimensional and semantic data models; and develop and administer Power BI reports, dashboards, and workspaces. Partner with stakeholders to define requirements and metrics, document lineage, support self-service analytics, and contribute to solution design, presales, standards, and reusable components.

Build and maintain data environments in AWS and Snowflake by identifying, preparing, manipulating, and modeling structured and unstructured datasets. Evaluate datasets for reporting and modeling, use data preparation tools such as Coalesce, resolve data quality issues, and advise stakeholders on data systems and concepts.

Analyze data quality, lineage, controls, and regulatory reporting processes; develop machine learning and predictive models; build automated data pipelines and workflows; create Tableau dashboards; apply data governance and validation practices; support audits and regulatory examinations; optimize databases and queries; and conduct end-to-end testing, UAT coordination, documentation, and deployment readiness activities.

Build and maintain the Air Force Data Mesh Environment through scalable ETL and ELT pipelines, data models, orchestration workflows, APIs, and governed data applications. Develop secure, maintainable software using Python, TypeScript, React, Node.js, SQL, and modern data tools. Collaborate with product, DevOps, security, and mission stakeholders while applying testing, validation, observability, and secure data-handling practices.

Lead a global team of data engineers and business analysts while architecting scalable data platforms, models, warehouses, and ETL/ELT pipelines. Develop SQL and Python data solutions, enforce governance and quality standards, resolve complex data issues, and support financial and operational reporting. Partner with stakeholders on forecasting, customer lifecycle analysis, performance tracking, reconciliation, and evolving business requirements.

Own the reporting and data capability for an Engineering Transformation Programme. Integrate engineering, workforce, product, and programme data; define metrics frameworks; build executive dashboards; establish reliable data pipelines; identify trends and data quality issues; and translate stakeholder needs into trusted, actionable insights. The role supports engineering leaders and may expand into self-service reporting, quality signals, and long-term Engineering Operations data strategy.