Top Remote Data Management Jobs

Analyze Medicaid and T-MSIS data to implement CMS payment model functions, including attribution, payment calculations, and quality measurement. Translate policy into specifications, SQL code, analytic files, and documented methodologies. Assess cross-state data quality, link Medicaid and Medicare sources, perform quality assurance, reconcile results, and communicate findings to technical and non-technical audiences. The role also involves code review, knowledge sharing, and supporting federal healthcare consulting engagements.

Leads and develops a team of Data Migration Engineers responsible for moving customer data into SaaS products. Provides technical guidance on SQL, ETL, data mapping, scripts, troubleshooting, and quality control. Manages project priorities, risks, costs, escalations, cross-functional coordination, global handoffs, and weekend coverage. Partners with engineering and operations teams to resolve defects and improve migration processes, tooling, automation, and cloud-based data platforms.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, segmentation, and LTV analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and deliver actionable recommendations. Requires strong SQL and Python skills, causal measurement expertise, marketing platform experience, and the ability to communicate insights to senior stakeholders.

Own developer relations for GPU-accelerated analytical data processing: integrate NVIDIA libraries into query engines and OLAP databases, produce rigorous benchmarks, create reference integrations and guides, collaborate with engine architects and NVIDIA product teams to define APIs and roadmap, and drive adoption across open source and commercial projects.

Lead and grow an enterprise data engineering team while owning the Snowflake and Airflow platform roadmap. Build reliable, governed data products and modernize pipelines using AI-assisted and agentic workflows. Establish standards for quality, observability, lineage, cost efficiency, incident response, and documentation while partnering with Analytics, Data Science, Product, and business stakeholders.

Senior Data Manager acquires, validates, integrates, protects, and manages enterprise data throughout its lifecycle. The role develops ETL solutions, data architectures, models, stored procedures, reporting datasets, and visualizations using Databricks, SQL, and cloud technologies. Responsibilities include translating business needs into technical requirements, leading data projects through deployment, analyzing complex data, improving processes, and supporting business decisions through accessible insights.

The Compliance Data & Analytics Manager develops AI and automation prototypes, integrates disparate data sources, builds Python and SQL analytics workflows, and creates web applications and automated alerts for compliance risk intelligence. The role partners with Legal, Finance, Regulatory Affairs, data science, and business intelligence teams to improve risk models and translate regulatory requirements into actionable analytical solutions.

Manages data engineering teams and leads architecture, design, and delivery across the data lifecycle. Builds scalable data infrastructure, pipelines, warehouses, and ETL processes using cloud and big-data technologies. Responsibilities include data modeling, system optimization, automation, analytics, predictive modeling support, and collaboration with business intelligence and data warehouse teams. The role also evaluates business needs, interprets trends, develops prototypes, and communicates analytical results to stakeholders.

Manages a data engineering team responsible for delivering and operating data platforms, pipelines, integrations, and data products. Oversees production reliability, security, governance, quality, cost, incident response, and platform lifecycle decisions. Leads hiring, coaching, performance management, workforce planning, stakeholder communication, and delivery prioritization. Applies engineering standards and improves automation, tooling, testing, documentation, and responsible AI-assisted engineering practices.

Manages a data engineering team responsible for delivering and operating data platforms, pipelines, integrations, and data products. Oversees delivery planning, production reliability, incident response, quality, governance, cost management, automation, and stakeholder communication. Hires, coaches, and performance-manages 4–8 employees while contributing to architecture, technical roadmaps, engineering standards, and responsible AI-assisted development practices.

Manages a data engineering team responsible for delivering and operating data platforms, pipelines, integrations, and data products. Oversees reliability, security, governance, quality, cost, incident response, and production support. Leads architecture input, delivery planning, automation, and continuous improvement while hiring, coaching, and performance-managing 4–8 employees. Partners with stakeholders to prioritize work, communicate risks and tradeoffs, and deliver trusted data capabilities.

Lead enterprise AWS-to-GCP data platform migrations, architect scalable lakehouse and streaming solutions, and oversee batch and real-time ETL/ELT engineering. Manage data modeling, BigQuery optimization, governance, security, quality, Terraform-based infrastructure, CI/CD, and cost optimization. Mentor data engineering teams, establish technical standards, manage delivery risks, and serve as the primary technical liaison for US-based stakeholders, BI, Data Science, DevOps, and business teams.

Manages business intelligence, data analysis, reporting, data governance, funding controls, and ERP-related initiatives for financial and grants management. Integrates data from financial, payroll, inventory, timekeeping, and payment systems; develops dashboards, metrics, forecasts, and federal and non-federal reports. Provides recommendations supporting budgets, capital funding plans, grant compliance, agency investments, and strategic decision-making. Requires collaboration with technical and nontechnical stakeholders.

Manages a data engineering team responsible for delivering and operating data platforms, pipelines, integrations, and data products. Ensures production reliability, security, governance, quality, observability, cost control, and incident response. Leads architecture and design participation, stakeholder communication, delivery planning, process improvement, and responsible AI-tool adoption. Hires, coaches, and performance-manages 4–8 direct reports while supporting workforce planning and resolving technical and operational risks.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, campaign reporting, experimentation, and lifecycle analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and generate actionable insights. Builds churn, retention, segmentation, and LTV models while evaluating paid acquisition and customer growth opportunities using SQL, Python, BI tools, marketing platforms, and modern AI-enabled workflows.

Leads the design, delivery, and optimization of data architecture, pipelines, semantic layers, data quality frameworks, and Tableau BI platforms. Partners with stakeholders to manage requirements, project plans, sprint execution, monitoring, governance, and release delivery. Guides data engineers, manages team members, and supports AI-enabled analytics capabilities. Requires strong SQL, Spark, PySpark, SSIS, cloud data platform, CI/CD, Tableau infrastructure, and data engineering expertise.

Leads and grows the data engineering team while owning enterprise data architecture, ingestion, modeling, semantic layers, governance, secure APIs, and reliable platform outcomes. Partners with finance, operations, sales, marketing, and product to deliver governed, traceable data for products and AI agents. Sets the roadmap, manages vendors and costs, reviews architecture, contributes code, and delivers cross-functional data platform projects.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Leads enterprise data strategy, analytics, business intelligence, data engineering, governance, and AI-enablement initiatives. Manages data and analytics teams, defines strategic roadmaps, develops executive dashboards and KPI frameworks, guides data warehouse and lakehouse architecture, and delivers advanced analyses, forecasting, and predictive insights. Partners with executives to improve operational efficiency, customer experience, revenue, profitability, and organizational performance while establishing scalable, governed, and reliable data capabilities.

Lead the data engineering team and oversee the DBA team while driving GCP-based streaming and batch data platform initiatives. The role combines people leadership with hands-on work across BigQuery, dbt, Apache Flink or Dataflow, data quality, testing, observability, governance, and cost optimization. The manager will promote AI-assisted engineering practices, ensure transactional database reliability, and align data infrastructure with business and product objectives.

Leads the data engineering squad and oversees the DBA team in a GCP-native environment. Drives streaming and batch platform initiatives using BigQuery, dbt, Apache Flink, and Dataflow; promotes AI-enabled engineering operations; ensures database reliability, data quality, testing, observability, governance, documentation, and cost optimization; and aligns data platforms with business and product goals.

Leads enterprise data governance strategy, policies, quality frameworks, data cataloging, lineage, security, privacy, master data management, and responsible AI governance. Partners with stakeholders to improve data trust, regulatory compliance, and business value. Provides technical expertise in Snowflake, data transformation, and complex SQL while driving adoption of governance standards and practices across the organization.

Lead and grow data engineering and analytics engineering teams while overseeing data platform modernization, BigQuery and Looker migrations, reusable dbt models, self-service capabilities, governance, reliability, and data foundations for AI and ML. Partner with technical and business stakeholders to prioritize roadmaps, manage hiring and performance, improve adoption, and protect sensitive healthcare data.

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.

Leads the data, analytics, and AI team; consolidates the company data warehouse; governs cross-system reporting and metric definitions; oversees pipelines, data quality, security, and upstream integrations; prioritizes the team backlog; manages and grows a distributed team; and delivers automation and AI tools. The role requires strong data engineering, analytics, BI, governance, business communication, and platform decision-making skills. Travel occurs approximately quarterly.