About the job
Job Title: Lead Data Steward
Location: Remote
Job Type: Full-time
Experience: 6-8 years
About Straive:
Straive is a prominent player in the realm of Content and Data Technology, offering comprehensive data services, domain expertise, and innovative technology solutions across various sectors. Our pillars include Data Analytics & AI Solutions, Data AI Powered Operations, and Education & Learning, all integral to our long-term vision. We specialize in delivering tailored solutions to business information providers in finance, insurance, legal, real estate, life sciences, and logistics. As a leading content services provider, we cater to research and education publishers, leveraging technology and AI alongside human expertise to create invaluable data assets. With a global client base spanning 30 countries and strategically located resources in eight countries including India, the Philippines, the USA, Nicaragua, Vietnam, the United Kingdom, and our headquarters in Singapore, we are committed to being the analytics and AI partner of choice for our clients.
Job Summary:
We are on the lookout for a Lead Data Steward to champion enterprise data initiatives, ensuring the integrity, completeness, and quality of data pipelines across our platforms. The ideal candidate will possess robust expertise in data quality practices and data management principles, instrumental in establishing reliable data foundations that enhance business intelligence, analytics, and operational reporting. This role is vital to our data team, supporting data governance and refining our data architecture.
Key Responsibilities:
- Define, document, and uphold clear and consistent business definitions, data standards, and business rules for key data elements within assigned domains.
- Ensure accurate and updated business metadata (e.g., data definitions, ownership, lineage, quality standards) is captured and maintained in the enterprise data catalog.
- Design and manage efficient ETL/ELT pipelines to ingest, transform, and deliver high-quality data across various systems.
- Implement the master data governance framework and utilize supporting data management tools.
- Establish data quality metrics and monitor performance against set targets.
- Conduct regular data governance reviews and suggest process improvements.

