09/08/2026 updated
100 % available
AI Adoption & Governance Lead | Technical Program Manager (Azure, Databricks, DORA)
Lisbon, Portugal
Only remote
BSc Management, Belarusian National Technical Univ.; BSc Economics, Belarus State Economic Univ.About me
Based in Lisbon, working across EU time zones. I am at my best where the hard part of a programme is not the build but getting it approved, adopted and operated: regulated environments, many stakeholders, real constraints. Direct and evidence-based — I flag early when a plan will not hold.
Azure DatabricksSAFeAgile MethodologyChange ManagementData GovernanceExtract Transform Load (ETL)Project ManagementScrum MethodologyStakeholder ManagementCertified Scrum MasterGenerative AIKanbanCloud MigrationDatabricks
I take generative AI from proof-of-concept into production inside regulated financial institutions — together with the governance that keeps it there.
Most GenAI programmes do not stall on the build. They stall on risk classification, control design and approval, and most teams find that out late. That gap is what I close.
What I have delivered:
• An LLM-based ELT documentation and compliance solution (Azure OpenAI, Databricks Unity Catalog, Streamlit) taken from prototype through AI risk assessment, human-in-the-loop control design, L1 architecture approval and CAB business case into production across 10 pipelines — cutting documentation and impact-analysis effort by around 80% and saving 35–40 engineering hours per month.
• Migration of 104 external data providers onto a pan-European Azure and Databricks data platform: 99% scope completion within the planned timeline, full lifecycle ownership from strategy through hypercare to handover.
• SaaS vendor governance under DORA — third-party security assessments, ITSCM/IRRP validation, audit remediation tracking — plus a cost-optimisation model that identified €53K of annual savings on one vendor.
• Delivery lead for an AI-powered enterprise search and text-analytics SaaS (Forrester Wave, Gartner Magic Quadrant), 40+ people, time to market cut from three months to one.
How I work: SAFe, Scrum and Kanban at programme scale, distributed teams of 20–40 across the EU, executive-level reporting. 15+ years, the last three of them on AI adoption and data platforms in financial services.
Core skills: AI Governance, Generative AI Adoption, Azure OpenAI, Databricks, Azure, Technical Program Management, Data Platform Migration, SAFe, DORA Compliance, Vendor Governance, ETL / ELT, Unity Catalog, Target Operating Model, Service Transition, Stakeholder Management.
Experience
Professional experience10+ years
Freelancer since2026
Languages
EnglishFluentPortugueseBasic knowledgeRussianNative speakerBelarusianNative speaker
Project history
Goal: move an LLM-based documentation and compliance solution from prototype into regulated production use, with governance that holds up to audit.
Scope: enterprise GenAI adoption plan for the IT organisation; initial AI risk assessment and classification; human-in-the-loop control design; L1 architecture stakeholder approval; CAB cost-benefit case; operational ownership model and KPIs.
Result: live across 10 ELT pipelines and repositories; documentation and impact-analysis effort reduced by around 80%; 35-40 engineering hours saved per month.
Stack: Azure OpenAI, Databricks Unity Catalog, Streamlit.
Goal: consolidate financial and rent-roll data from 104 external service providers onto a single Azure-based platform serving multiple European jurisdictions.
Scope: migration strategy, phased rollout roadmap and programme milestones; SAFe programme across data engineering, vendor integration and downstream consumers; 20+ distributed team; release management, service transition and target operating model; ITSM service desk launch; DORA-aligned third-party security assessments and ITSCM/IRRP validation.
Result: 99% scope completion within the planned timeline; account NPS of 100 sustained since H2 2024; €53K annual savings identified through a vendor cost-optimisation model.
Stack: Azure, Databricks, Apache Spark, Data Lake, Kubernetes, Azure Data Factory, event-driven integration.
Goal: deliver and operate a B2B SaaS platform for enterprise search and text analytics serving Fortune 500 clients; the product was recognised in the Forrester Wave and the Gartner Magic Quadrant.
Scope: delivery and operational governance across SaaS, iOS/Android apps and REST APIs with 40+ people; roadmap and OKR planning; release management framework; ITSM support model redesign; PoCs and client implementation projects for enterprise customers.
Result: time to market reduced from three months to one; resolution time for high-complexity requests cut from 40 to 18 hours; service-request resolution compliance raised from 80% to 98%.