Analytics Engineer

• Direct and develop a small Analytics Engineering team partnering with Analysts and Data Engineers
• Define data modeling standards and dbt best practices across the healthcare.gov outreach team
• Oversee migration, refactoring, and optimization of complex SQL logic into scalable dbt pipelines
• Build dimensional models and semantic layers to answer business questions and track KPIs
• Develop and manage complex Airflow DAGs to orchestrate data movement
• Implement data quality checks and monitor data reliability
• Maintain documentation for datasets, lineage, and transformation logic
• Drive query performance and data model efficiency; optimize Snowflake usage
• Develop understanding of federal healthcare programs, ACA policy mandates, and regulatory frameworks
• Resolve challenges independently and deliver accurate products on short deadlines
• Complete other reasonable tasks assigned by management

Manager II, Analytics Engineering

• Lead complex risk data analytics initiatives from requirements through delivery, validation, implementation, and operational support
• Translate business questions into clear data, analytics, and reporting requirements
• Build and maintain trusted risk datasets, semantic models, dashboards, reports, and reusable analytics products
• Integrate and analyze information from internal platforms, vendor tools, external sources, and operational systems
• Automate data collection, preparation, validation, reconciliation, refreshes, and recurring reporting processes
• Produce analytics identifying trends, control weaknesses, emerging risks, performance gaps, and opportunities to prioritize risk action
• Support governed, analytics-ready data products for leadership reporting, operational insight, automation, and future AI-based capabilities
• Establish best practices for data sourcing, transformation, quality, documentation, governance, access, and reporting adoption
• Coach analysts and enable stakeholders through technical consultation, reusable templates, training, and practical guidance
• Partner with risk, control, regulatory, product, engineering, and leadership stakeholders
• No supervisory duties

Data Analytics Engineer

• Design and build a layered, version-controlled data model in BigQuery using Dataform
• Migrate business logic from ad hoc scheduled queries into governed, reusable, tested models
• Define and own canonical definitions of core business metrics
• Implement automated data testing and continuous integration
• Document models, definitions, and data lineage
• Partner with analysts and business stakeholders to support reliable self-service reporting
• Partner with IT to support accurate and timely raw data ingestion
• Help evaluate and shape the future BI layer
• Report to the Senior Manager of Data Analytics
• Build SupplyHouse.com's production data layer and make high-quality data accessible and actionable across the organization

• Build and ship internal web tools and dashboards on top of existing Zoox data sources, from prototype through production use
• Work with TPMs and engineering partners to turn business questions into scoped tools and iterate based on feedback
• Design queries, transformations, and pipelines to keep data current without manual intervention
• Extend geospatial tooling with new attributes, data sources, and visualizations to inform service expansion
• Surface data coverage gaps, flag low-confidence results, and make tool assumptions visible
• Partner with leaders across vehicle development, software engineering, manufacturing, fleet operations, and business functions to support corporate milestones
• Own a piece of the stack end to end and deliver tools for program and executive reviews

Program Tooling & Analytics Developer

• Explore current Advanced Analytics topics, including the modern data stack, unified data platforms, Advanced Analytics algorithms, and Data Mesh
• Study the related technology tools and methodologies
• Deliver a concrete use case and write a summary article for internal sharing and/or publication through Wavestone’s channels
• Actively participate in client engagements as part of project teams
• Analyze business challenges and contribute to defining relevant performance indicators
• Contribute to the deployment of robust analytics solutions
• Develop expertise in the BI and Analytics solutions and platforms available on the market
• Contribute to the firm’s internal activities
• Help drive and develop the Data, Analytics & AI offering
• Write thought leadership content, including articles and presentations
• Participate in recruitment, university relations, and training initiatives
• Build technical demonstrators and test new Data & Analytics solutions

Data Platform, Analytics Engineering, Data for AI

• Design and implement complex data models for dashboards, self-serve analytics, and data science teams
• Write highly tuned, scalable SQL queries over large-scale, heterogeneous data warehouses
• Enable AI tooling with semantic layers and observability
• Guide analytics functions on best practices
• Manage and improve data infrastructure for data-driven decision-making solutions
• Help develop front end applications exposing analytical datasets enterprise-wide
• Work with Product and Corporate Engineering system teams to structure source systems for reporting consumption across the enterprise

Senior Analytics Engineer

• Act as a subject matter and business intelligence expert for customer analytical technologies
• Drive adoption of self-service analytics capabilities
• Develop and implement data models, datasets, reports, dashboards, and integrated analytics solutions
• Consult with the Data Product Owner on Data Lake design
• Establish, promote, and monitor adherence to business intelligence policies and standards
• Partner with IT on the stability, performance, upgrades, and roadmaps of user-facing technology
• Develop understanding of customer business processes, enterprise platforms, and table structures to extract and report accurate data
• Create strategies for interactive dashboards and promote stakeholder adoption
• Manage personal and team deliverables, timelines, and project milestones
• Shape and extract data using coding languages including Python and SQL
• Apply advanced data quality monitoring approaches
• Identify applications of data science for predictive and prescriptive solutions
• Act as an internal consultant to advance analytics maturity, provide strategic insights, identify opportunities, develop strategies, and mentor staff
• Lead complex data product and visualization development
• Mentor and coach team members, review and delegate work, and guide technical design and implementation
• Support emergency response efforts, potentially outside normal hours or typical responsibilities

Lead Data and Analytics Developer – Customer Analytics

• Design and maintain dbt data models for support data, covering ticketing, KYC, withdrawals, and market resolution workflows
• Integrate data from sources including Decagon, BPO partners, CRM systems, and WFM tools into the cloud warehouse
• Build data quality tests, monitoring, and documentation so the team can trust what they're looking at
• Define shared metrics and dimensions in the semantic layer so CX, ops, and leadership are all calculating things the same way
• Build dashboards and self-serve datasets that let CX teammates answer operational questions without waiting on a data request
• Identify recurring failure patterns in support data and translate them into documented findings for the CX playbook and internal stakeholders
• Partner with the central data engineering team on shared pipelines, infrastructure decisions, and data governance

Analytics Engineer, Customer Experience

• Develop, test, deploy, and maintain data structures, enterprise reports, dashboards, data visualizations, analytical systems, and recurring performance reporting
• Analyze contact center, IVR, digital, customer, and operational data to identify trends, root causes, performance gaps, and opportunities for automation or process improvement
• Build and maintain reporting using Power BI, SQL, Amazon QuickSight, Amazon CloudWatch, and other approved analytics tools
• Monitor key operational metrics and proactively identify emerging issues, including changes in call volume, containment, transfer behavior, speed of answer, abandonment, handle time, call drivers, and customer experience indicators
• Partner with operations, workforce management, IVR, automation, product, technology, data architecture, and data governance teams to understand business problems, define requirements, and design practical data solutions
• Research and query structured and unstructured data sources to understand how systems work, validate data quality, and expand available reporting and insight capabilities
• Create clear documentation for code, reporting logic, metric definitions, source systems, data structures, and operational assumptions
• Serve as a data steward for assigned subject areas, helping ensure data is accurate, available, properly classified, and governed appropriately
• Communicate findings clearly to technical and non-technical audiences, translating complex data into practical business recommendations

Contact Center Data Analytics Engineer, I

• Hire, onboard, develop, and manage the performance of the analytics and data engineering team
• Set the technical bar and own the dbt repository as a shared platform
• Review pull requests, make architecture decisions, and establish standards for tests, documentation, CI, and change control
• Own the Snowflake warehouse, dbt, source ingestion and freshness, external tables, and AWS Glue/CDK jobs
• Model core business domains including orders, contracts, claims, and service orders
• Lead platform migrations, including moving dbt execution to Snowflake-native tooling, and retire legacy components
• Partner with internal stakeholders to translate business questions into models and shape roadmap priorities
• Improve data quality through schema validation, freshness monitoring, and critical-service audits
• Run on-call, monitoring, alerting, incident triage, and root-cause follow-through
• Enable self-service through documentation, semantic consistency, and BI access
• Automate operational work using AI-assisted workflows for alert triage, refresh requests, and file processing
• Report to the Senior Engineering Manager, Data Engineering while leading a remote team

Analytics Engineer jobs in United States

Manager II, Analytics Engineering

Data Analytics Engineer