Top Remote Data Management Jobs in Salt Lake City, UT

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

Oversee data ingestion and transformation, manage finance data sources, ensure scalability and reliability of data platforms, and collaborate with stakeholders to align data engineering with organizational goals.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Build and lead the Compliance data function, owning data products for Customer Risk, Transaction Monitoring, AML, Sanctions. Design and implement data risk frameworks (quality, usage, lineage), enforce data contracts between producers and consumers, maintain end-to-end lineage with Engineering, and drive a risk-based data roadmap aligned with compliance requirements.

Lead data-driven strategies and analytics at PwC, motivating teams to improve business performance and decision-making through advanced techniques and collaboration.

As a Manager in Data Governance at PwC, you will lead data governance strategies, supervise teams, manage client accounts, analyze data for decision-making, and ensure compliance with standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Senior Manager in Data and Analytics Engineering at PwC, you'll design data architecture strategies for healthcare, mentor teams, influence stakeholders, and enhance healthcare operations through data solutions.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

Manage Finance-focused data products, collaborating with teams to define requirements and drive delivery for financial analysis and reporting.

Lead and manage a team of data engineers to develop and maintain data pipelines and warehouses, ensuring data quality and adherence to best practices.

The role involves leading the data governance team, managing IT projects, collaborating with stakeholders, and ensuring continuous improvement of processes. Responsibilities include strategic planning, client management, and optimizing resource utilization, while overseeing governance activities and departmental management.

The role involves managing Data Success Managers, overseeing data operations, supporting partners, and improving team workflows to deliver quality service.

Lead a team of data and infrastructure engineers to develop cloud-based data systems and solutions for analytics, ensuring best practices in data management and MLOps.

The Senior Manager of Data Insights will lead a team to optimize data sharing and governance, develop data strategies, and deliver actionable business intelligence across Docker's operations.

Support data-driven operations by sourcing, integrating, validating, and transforming data; build and maintain dashboards, agency scorecards, and reports; analyze KPIs to identify improvement and cross-sell opportunities; measure campaign effectiveness; ensure data quality; partner with stakeholders and technology teams; support monthly reviews and ad-hoc analytics requests.

Lead acquisition, cleaning, validation, storage and protection of datasets to support reporting and analysis. Design and execute data architectures, policies and ETL pipelines (SSIS, ADF) using cloud platforms (Azure Synapse, Snowflake, Databricks). Deliver dimensional models, visualizations, and lifecycle governance to make complex data accessible and drive business insights.

The Manager R&D Data Steward oversees data governance, quality management, and stewardship of R&D data, ensuring compliance and integrity across data products and leading a team to support data management initiatives.

As a Senior Manager, R&D Data Steward, you will oversee stewardship of Pharma R&D data, ensuring data quality and compliance across the R&D value chain. You will lead a team, operationalize data governance, and drive improvements in data management processes, leveraging automation and cross-functional collaboration to enhance data-driven insights.

The Data Product Manager defines the reporting roadmap, manages KPI alignment, collaborates cross-functionally, ensures data quality, and leads continuous improvement in analytics processes.

Serve as the primary bridge between IT and business partners to translate data needs into actionable data products (DaaP). Design enterprise data models, recommend technical solutions, oversee functional requirement translation to development-ready stories, ensure governance/security compliance, validate UAT, and communicate impact to stakeholders while supporting the ADI program.

The Senior Manager will oversee operations across technology functions, lead strategic initiatives in Data & AI, and ensure effective management of technology projects and resources. Responsibilities include engaging stakeholders, identifying risks, and fostering continuous improvement within the team.

As a Curriculum Manager, you will manage the content development lifecycle, collaborate with instructors, and improve data science curriculum, ensuring technical accuracy and pedagogical effectiveness.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

Own the conversion of raw alternative datasets into scalable, AI-ready data products. Define methodologies, metric definitions, and guardrails for AI-driven insights. Partner with engineering, product, and customers to expand use cases, ensure data quality, validate changes, and manage incidents to deliver reliable, explainable data products at scale.