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Alex Multykh
Co-founder @Dataforest
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WORK HISTORY
Co-founder @Dataforest
Kiev, UA
Built and scaled Dataforest as a data engineering & AI partner for logistics, platform, and ops-heavy businesses where growth exposed hidden data costs faster than teams could react.Key impact & outcomes: Eliminated fragmented data realities by designing unified operational data platforms that reconciled WMS / TMS / ERP and internal systems into one decision-ready view→ fewer conflicting numbers, faster alignment across teams, lower coordination cost Architected real-time data ingestion and ETL pipelines for high-volume environments where batch reporting created blind spots→ earlier visibility into exceptions, reduced revenue leakage during peaks Connected product usage signals to operational and financial outcomes, allowing teams to understand why behavior changed — not just that it did→ better prioritization, fewer misaligned product decisions, higher ROI per feature shipped Reduced decision latency and operational escalations by removing manual reconciliation from Ops workflows→ lower cost-to-serve, fewer firefighting cycles, more predictable margins Enabled C-level leadership to operate on a single source of truth instead of reconciled reports and escalations→ faster executive decisions, earlier risk detection, stronger control at scaleCore focus areas: Operational data architecture & system unification ETL / ELT pipelines, data warehouses, and real-time analytics AI & predictive analytics for risk, capacity, churn, and anomalies BI dashboards built for trust, explainability, and action — not vanity metrics
EDUCATION
Taras Shevchenko National University of Kyiv
Master's degree, Finance
ABOUT ALEX MULTYKH
As Co-Founder of Dataforest, I’ve spent 7+ years building and scaling Data Engineering and AI architectures for companies where procurement complexity, supply volatility, and growth expose data weaknesses first.Dataforest was built around a simple principle: supply chain resilience only works when data remains trustworthy under real operational pressure.Across procurement, supply chain, retail, manufacturing, and platform businesses, the same pattern appears at scale: supplier, ERP, and inventory signals don’t reconcile ETL jobs run, but planning context is fragmented dashboards explain what happened, not what supply teams should do next teams compensate manually while decision latency growsNothing crashes — yet resilience, margins, and confidence quietly erode.At Dataforest, my role has been to design and scale unified operational data foundations that allow procurement, supply chain, finance, and leadership teams to operate on one shared reality — without replacing core systems.What I build at DataforestData Engineering & Integration End-to-end ETL / ELT pipelines, data warehouses, and data lakes API-based data integration across ERP, procurement suites, WMS, supplier portals, and internal systems Real-time and batch pipelines designed for scale, governance, and explainabilityAI, ML & Predictive Analytics Predictive analytics for supplier risk, demand volatility, replenishment, and anomalies Machine learning models grounded in clean, unified procurement and inventory data AI systems designed to support planning and sourcing decisions, not generate noiseAnalytics, BI & Decision Systems Supply chain and procurement analytics platforms BI dashboards focused on trust, explainability, and action Analytics embedded into sourcing, planning, and inventory workflowsAutomation & DataOps Automation built on reliable supply and procurement data foundations DataOps practices for stability, cost efficiency, and control Architectures that reduce manual reconciliation and firefighting across supplier and planning teamsFounder perspectiveMost companies don’t lack supply data — they lack supply data agreement.When procurement, planning, inventory, and finance operate on fragmented signals: teams debate numbers instead of acting planners absorb the cost through manual effort leadership reacts late to supplier and inventory riskWhen data engineering is done right, clarity arrives earlier, decisions get calmer, and resilience becomes scalable.That’s not process maturity.It’s architecture.
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