Technical Program Manager - Data & AI Platform

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Kapitus is building a next-generation enterprise data and AI capability that replaces hundreds of legacy analytics workflows with governed, reusable business data products and moves the organization from producing reports to producing decisions.

We are looking for an experienced Technical Program Manager to run the day-to-day execution of this program. This is not a status-reporting role. You will own the integrated delivery plan across concurrent workstreams data architecture and platform engineering, governed business data products, MDM and ontology, semantic and consumption layers, data governance, ML/MLOps, and GenAI/agentic AI. This will be delivered by a mix of internal teams and external consulting partners across onshore, nearshore, and offshore locations. You will keep a milestone-gated, tightly budgeted program on schedule, hold vendors to their commitments, and be credible in front of both engineers and executives.

You report directly to the program executive leading the Data & AI organization and serve as the operational execution lead for the transformation program. You are not the architect, the product owner, the engineering lead, or the Scrum Master; you are the person who turns architecture decisions into plans, plans into accountable work, and work into accepted, evidenced outcomes.

What you will do:

Program delivery and planning

Own and maintain the single authoritative integrated delivery plan across internal teams and all delivery partners — reconciling vendor plans into one program baseline rather than managing independent project schedules — covering sequencing, cross-stream dependencies, critical path, resource loading and capacity, and milestone readiness.

Own end-to-end readiness of each data product delivery wave: design, source readiness, build, governance controls, verification, business validation, serving and consumption readiness, production release, and legacy decommissioning. Code complete is not product complete.

Run the program’s delivery cadence end to end — sprint ceremonies, backlog alignment across streams, RAID (risks, actions, issues, decisions) management, and weekly executive reporting.

Manage milestone-gated delivery: track work against defined acceptance gates (design baseline, ready-to-build, vendor verification, business/customer validation, production acceptance, closeout) and make sure nothing is claimed complete that has not passed its gate.

Track budget consumption against not-to-exceed envelopes and estimate-at-completion forecasts; surface variances early with options, not surprises.

Plan realistically — distinguish contracted capacity from productive capacity, and keep utilization assumptions explicit and defensible.

Vendor and partner management

Manage day-to-day execution with external consulting partners: statement-of-work scope, deliverable acceptance, change control, and separation of duties between build and validation teams.

Coordinate distributed teams across time zones and keep handoffs between onshore design and offshore engineering clean.

Prepare the evidence trail — delivery plans, defect logs, verification records — that supports invoice approval and milestone release decisions.

Maintain traceability from contractual deliverables through program requirements, epics and stories, acceptance criteria, validation evidence, milestone gates, and commercial approvals — so every invoice correlates to accepted work.

Technical coordination

Understand the work well enough to challenge it: data product design, ELT pipeline builds, legacy workflow migration and decommissioning, semantic layer and BI serving, data quality and governance controls, and ML/AI platform components.

Run dependency management between platform hardening, data product waves, governance readiness, and AI platform build-out.

Coordinate governance readiness alongside product delivery — ownership, critical-data-element and sensitivity classification, lineage, data quality controls, metadata, policy conformance, and catalog readiness — so governance ships with the product rather than following it months later.

Manage cross-functional readiness dependencies spanning architecture, security/risk, platform, governance, procurement, and production operations; ensure approvals and unresolved decisions are visible on the critical path and driven to closure.

Facilitate architecture and design decision forums; track exceptions and conformance debt to closure.

Stakeholder management

Be the connective tissue between business owners, architecture, engineering, governance, finance/procurement, and executive sponsors.

Communicate program status in plain language — accurate, evidence-based, and honest about risk.

Drive closure, not coordination alone: drive timely decisions, identify accountable owners, escalate unresolved dependencies early, and challenge unsupported status or completion claims.

What we’re looking for:

10+ years delivering complex technology programs

5+ years leading enterprise data/platform transformation programs involving multiple concurrent technical teams. Backgrounds that combine program leadership with engineering management, technical delivery, consulting, data platform delivery, or solution delivery are strongly valued; demonstrated technical delivery depth matters more than title progression alone.

Consulting or professional services delivery background. You have run client-facing programs under tight timelines, fixed budgets, and contractual milestone commitments, and you know how to manage scope, change control, and acceptance in that environment (either side of the table: delivery firm or client program office).

Deep, practiced agile delivery skills with Scrum, Kanban, and scaled/hybrid models; you can run ceremonies, coach teams, manage backlogs across multiple pods, and blend agile execution with milestone-gated commercial governance.

Modern cloud data platform fluency and hands-on program experience with Snowflake and/or Databricks, and working knowledge of the surrounding ecosystem: ELT/transformation frameworks (e.g., dbt), orchestration, data catalogs and lineage tooling, BI/semantic layers, and legacy workflow migration (e.g., Alteryx, SSIS, or similar).

Strong AI/ML literacy, you understand the ML lifecycle (feature engineering, training, validation, deployment, monitoring), MLOps concepts, and the emerging GenAI/LLM stack (retrieval, agents, model risk considerations) well enough to plan and de-risk AI workstreams.

Vendor management experience managing multi-vendor delivery against SOWs, rate cards, and NTE budgets, including offshore/nearshore delivery models.

Financial discipline comfortable owning budget tracking, forecasting (EAC), and the commercial mechanics of T&M and milestone-based contracts.

Excellent communication skills with crisp written and verbal communication; able to produce executive-ready status materials and defend the numbers behind them.

Additional Preferred S