Dmytro K.

Project overview:

End-to-end cloud data platform for a fast-growing neobank, with core event tables ingesting large volumes of data daily and hundreds of terabytes of cumulative data. The platform underpins reconciliation, partner integrations, and financial reporting – operating on a cloud-managed infrastructure where both reliability and regulatory compliance are non-negotiable. Given the nature of the business, any pipeline failure or data discrepancy carries direct financial and audit consequences.

Responsibilities:

• Migrated reconciliation and partner-integration pipelines from self-hosted to cloud-managed orchestration, eliminating a category of infrastructure failure and freeing the team from operational toil.
• Designed automated reconciliation pipelines for primary payment gateways, replacing error-prone manual processes with auditable, testable data flows.
• Built internal tooling for DAG generation and operational workflows, establishing consistent patterns that reduced both development time and onboarding friction across the team.
• Refactored legacy workflows using modular DAG patterns and dynamic task mapping, achieving substantial gains in average execution time.
• Worked closely with finance and compliance teams to ensure data automation aligned with regulatory requirements.

Achievements:

Automated reconciliation pipelines for primary payment gateways, eliminating the vast majority of manual effort and materially strengthening audit readiness. Built internal tooling for workflow generation that became a team-wide standard, significantly compressing the time from requirement to deployed pipeline. Improved execution times on core legacy workflows through architectural refactoring. Designed a data compaction system for cold storage that reduced both read latency and long-term maintenance costs. Identified and resolved a query efficiency issue that measurably reduced cloud compute consumption at the organizational account level — one of the more impactful cost engineering wins on the platform.

Technology stack:

Project overview:

Mission-critical market data platform covering equities across dozens of global exchanges, FX, and fixed income, serving both internal quantitative trading desks. At this scale and in this context, reliability is not a quality attribute – it is the product. The system had to sustain consistent ingestion and low-latency access during peak trading hours across multiple time zones, with no tolerance for data gaps or processing delays that could affect trading decisions.

Responsibilities:

• Owned end-to-end reliability of ETL processes ingesting global equities data and delivering analytics to internal trading systems – a zero-defect-tolerance environment with direct business exposure on failure.
• Built ingestion and analytics pipelines for FX and fixed income asset classes, expanding the platform's cross-asset coverage and enabling unified analytics across instrument types.
• Led a performance engineering initiative on the primary real-time streaming ingestion component, improving startup time and processing stability during peak load periods.
• Developed an ETL migration framework to automate transition from legacy warehouse infrastructure to a modern architecture, reducing per-pipeline development effort dramatically and accelerating onboarding of new engineers.
• Delivered storage format optimizations that reduced memory consumption during ETL execution; coordinated phased production rollouts with distributed teams across multiple time zones.

Achievements:

Substantially reduced average processing time on the core ingestion pipelines, directly improving throughput for downstream trading systems. Delivered a custom streaming sink integration that cut both average and peak query latency by a significant margin – the peak reduction being the more operationally important figure, as it addressed the tail-latency spikes that affected time-sensitive analytics. Improved the startup behavior of the main streaming component, eliminating instability during high-load trading windows.

Technology stack:

Project overview:

Data platform modernization for one of the largest US retail chains, spanning over a thousand physical stores and a major e-commerce operation. The scope covered legacy warehouse migration, recommendation pipeline rebuilding, and data governance implementation – all across a petabyte-scale environment without disrupting ongoing retail analytics.

Responsibilities:

• Technical migration of an enterprise data warehouse from multiple legacy database systems to a cloud-native solution, preserving full metadata and lineage coverage across petabyte-scale retail data.
• Migration of a substantial share of ETL pipelines for recommendation and user interaction systems from legacy distributed processing to modern cloud orchestration and warehouse infrastructure.
• Refactored the internal DAG-building framework, meaningfully improving maintainability and reducing the cognitive overhead of pipeline development across the team.

Achievements:

Reduced average processing time on recommendation and user interaction pipelines after migrating them to modern cloud infrastructure. Built a disaster recovery tool for the metadata and lineage environment that became the standard cross-team onboarding reference, replacing a previously manual and undocumented process.

Technology stack: