Role Overview
We are looking for a Senior Database Administrator to own the reliability, performance and ongoing improvement of our client's newly built point-of-sale (POS) database, including its product and customer master data. The role will help turn a clean, well-structured database into a trusted foundation for business analytics, AI, generative AI and data science. Alongside day-to-day database administration, you will establish the controls, ownership and workflows needed to keep data accurate, consistent, secure and usable as volumes and use cases grow, working closely with data engineers, analysts and
data scientists to make the POS database dependable for both operational needs and advanced analytical applications.
Key Deliverables & Responsibilities
1. Database Administration & Reliability:
Manage and optimize a large-scale database. Own database health, availability, capacity planning, indexing, query performance and workload management. Diagnose bottlenecks and support growing data volumes and increasingly complex analytical workloads.
Protect reliability and recoverability. Establish monitoring, alerting, backup and recovery procedures, tested disaster recovery plans and clear incident response processes.
Protect operational performance as usage expands. Design suitable workload isolation, replication or analytical access arrangements so reporting, experimentation and AI-driven queries do not compromise core database services.
2. Master Data & Data Quality:
Maintain trusted POS and master data. Strengthen the structure and integrity of POS transactions, product master data and customer master data. Manage keys, relationships, duplicates, missing values, inconsistent classifications and changes to master records over time.
Build lasting data-quality controls. Implement automated validation, reconciliation, quality monitoring and exception handling. Detect issues at ingestion and throughout the data lifecycle, with clear escalation and remediation workflows.
Establish ownership and controlled workflows. Partner with business owners to define who can create, change, approve and correct data. Put in place documented processes for master data updates, schema changes, access requests and issue resolution.
3. Analytics, Data Science & AI Enablement:
Make data ready for analytics and data science. Work with data engineers and analysts to provide documented schemas, reusable views, reliable historical data and efficient access patterns for reporting, customer and product analysis, forecasting and other analytical use cases.
Enable safe AI and generative AI access. Collaborate with AI teams to provide governed datasets and controlled query access. Support approved use cases with appropriate permissions, sensitive-data protections, traceability and workload limits.
4. Security, Governance & Documentation:
Maintain security and governance. Implement least-privilege access, audit logging, retention controls and appropriate protection of customer information in partnership with security and governance teams.
Keep the database understandable and maintainable. Maintain data dictionaries, relationship diagrams, business definitions, lineage documentation and operational runbooks. Recommend and deliver improvements to database design and administration practices.