Director of Data Platforms & Governance
New
United StatesFull-TimeDirector
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSSQLETLData engineeringData modelingData analytics
Requirements
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
- 10+ years of experience in data engineering, analytics infrastructure, or enterprise data platform leadership roles.
- Strong expertise with AWS data technologies such as S3, Glue, Athena, Redshift, and Lambda.
- Proven experience designing and scaling cloud-based data platforms and modern data architectures.
- Hands-on experience building and optimizing ETL/ELT pipelines and integrating data from APIs, telemetry sources, and enterprise SaaS platforms.
- Deep understanding of data governance practices, including metadata management, access controls, data lineage, and compliance standards.
- Advanced SQL, data modeling, and analytics infrastructure expertise with experience supporting BI, AI/ML, or data science teams.
- Experience integrating enterprise systems such as Salesforce, ERP platforms, finance systems, and customer success tools.
- Strong leadership, communication, and stakeholder management skills with the ability to influence executive-level decision-making.
- Ability to thrive in fast-paced, high-growth environments while managing complex, cross-functional initiatives independently.
Responsibilities
- Define and execute the long-term strategy, architecture, and operational model for the company’s cloud-based data platform to support analytics, AI, and business intelligence initiatives.
- Lead and mentor a high-performing data platforms organization, establishing clear objectives, accountability structures, and scalable engineering practices.
- Build and oversee robust ETL/ELT pipelines integrating data from product telemetry, APIs, SaaS applications, and enterprise systems.
- Establish and maintain comprehensive data governance frameworks, including data ownership, lineage, access management, quality standards, and lifecycle policies.
- Drive the development of curated datasets and scalable data models to improve operational reporting, analytics accuracy, and machine learning readiness.
- Collaborate closely with cross-functional stakeholders across Product, Engineering, Security, Finance, and Operations to ensure seamless data integration and platform reliability.
- Implement processes and monitoring systems that improve data quality, pipeline performance, compliance, and operational efficiency across the organization.
- Support AI and advanced analytics initiatives by ensuring the availability of secure, reliable, and well-structured data infrastructure.
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