Top Remote Data & Analytics Jobs in Detroit, MI

Own go-to-market funnel reporting, dashboards, data quality, and analysis for Sales, Marketing, and RevOps. Integrate CRM and marketing data, monitor lead scoring and attribution models, investigate funnel performance, identify root causes, and provide actionable recommendations. Maintain reporting systems, audit data hygiene and tool adoption, and communicate insights clearly to non-technical stakeholders in a remote-first SaaS environment.

Leads the architecture, development, and maintenance of scalable batch and real-time data pipelines, cloud data platforms, ETL/ELT workflows, and ML data infrastructure. Establishes standards for data quality, observability, lineage, governance, security, and modeling. Partners with data scientists, engineers, DevOps, and business stakeholders; evaluates technologies, drives technical initiatives, mentors engineers, and supports production-grade APIs and machine learning pipelines.

Senior Data Analyst responsible for integrating sales, CRM, customer, campaign, product usage, and engagement data into forecasts, dashboards, recurring reports, datasets, and actionable insights. The role analyzes pipeline performance, customer health, product adoption, retention, churn, marketing campaigns, and commercial trends. It partners with cross-functional stakeholders, defines KPIs, improves data quality, prepares executive reporting, and supports strategic decision-making through clear analysis and recommendations.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Leads advanced analytics initiatives supporting Tribal healthcare programs and strategic decision-making. Develops predictive and prescriptive models, integrates complex data sources, establishes data quality frameworks, evaluates healthcare data resources, and produces reports and dashboards. Coordinates cross-functional projects, ensures federal compliance, supports quality measurement, mentors junior staff, and may serve as a team lead.

Lead Data Engineer responsible for designing, maintaining, and improving data pipelines and platform infrastructure across ingestion, streaming, transformation, and delivery. The role uses Airflow, Spark, Iceberg, Kafka, Flink, dbt, Terraform, and GCP. Responsibilities also include defining data contracts, documenting systems, optimizing performance, reviewing code, supporting access controls and compliance, and collaborating with stakeholders and teammates.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Own end-to-end financial data analysis, including SQL and dbt data modeling, Looker dashboard development, data quality validation, reconciliation, and stakeholder-facing insights. Partner with Finance and Payments to support reporting, planning, revenue analysis, cost insights, and transaction-level analytics. Translate ambiguous business needs into reliable technical solutions while improving documentation, QA, modeling, and data architecture practices.

Analyze healthcare, financial, and utilization data to identify inefficiencies, support data-driven decisions, maintain dashboards, build analytical models, communicate insights, and monitor improvement initiatives across the organization.

Own client relationships and analytics strategy across digital measurement and GEO initiatives. Architect, audit, and QA GA4 and Adobe Analytics implementations; develop measurement frameworks, dashboards, experimentation plans, and causal analyses. Present insights to executive stakeholders, manage analytics workstreams, mentor teammates, and build AI-assisted workflows, automations, and scalable systems. The role combines traditional analytics with measurement of AI-powered search visibility and customer behavior.