Oracle DBA / Analyst

Your mission is very practical: inspect real Oracle data structures, uncover retention gaps, identify personal data fields and triggers, and help shape retention rules + cleanup/automation approaches that stand up to compliance expectations. You'll also bring AI-assisted ways of working to accelerate large-scale analysis and classification. 

MUST-HAVE SKILLS

• 10-15 years of consulting-level expertise with Oracle Database technologies ​​​​(senior SME level).
• 10+ years of hands-on experience analyzing Oracle database structures: schemas, metadata, object relationships, dependencies.
• Strong practical ability to perform inspections, document dependencies, and interpret object relationships in complex Oracle environments.
• Confident usage of Oracle SQL Developer (or similar Oracle analysis utilities) for deep-dive structural and metadata analysis.
• 3-5+ years applying AI-assisted practices to accelerate data analysis (e.g., faster scanning, pattern identification, data classification in database inspection workflows).
• Solid understanding of GDPR data retention requirements, including the principles of data minimization, purpose limitation, storage limitation, and compliance reporting - and the ability to translate them into technical retention logic and data lifecycle structures in enterprise data environments (5-10+ years expected at senior level).
• A strong, structured approach to identifying high-risk datasets, retention triggers, and retention-related gapsin large-scale database landscapes.
• A mindset for spotting automation opportunities in cleanup and retention enforcement workflows (especially when scale gets painful).

ARE YOU WILLING TO...

• Get hands-on quickly: analyze real Oracle structures and metadata, not just talk about them.
• Work in a high-trust senior consultant mode: clear documentation, crisp recommendations, and practical guidance that teams can implement.
• Be ready and committed for regular weekly client onsite office visits in Tallinn and collaborate closely with internal teams.
• Drive outcomes in a complex enterprise setting:
• Identify personal data fields, retention triggers, and high-risk datasets
• Recommend retention rules, cleanup approaches, and automation opportunities
• Support deployment of "Tidy"-aligned retention logic / cleanup workflows
• Bring AI-enabled efficiency into large-scale analysis tasks