Key Strengths — Cloud Etl/Elt & Data Platform
- Enterprise ETL/ELT at scale: designed and operated batch and streaming pipelines moving millions of records per week from operational source systems into curated analytical datasets — landing/staging/curated (bronze/silver/gold) layering, incremental and idempotent loads (MERGE), data cleansing, profiling, reconciliation and quality gates.
- Dual-cloud delivery: Azure — Databricks, Data Lake Storage Gen2, Delta Lake, Unity Catalog; AWS — S3, EMR (Spark), Lambda, Step Functions, Kinesis, SQS/SNS, DynamoDB, SageMaker, CloudFormation. Comfortable mapping equivalent services across both stacks.
- Legacy-to-cloud migration: migrated an undocumented on-premise ETL estate (Autosys, shell, SQL, Python) into cloud pipelines with parallel-run reconciliation, controlled cutover and zero disruption to downstream consumers — the same discipline a database-migration or SSIS-modernisation programme requires.
- Structured, semi-structured and unstructured data: relational extracts (Oracle, SQL Server, PostgreSQL), XML/JSON/CSV landing files, Parquet/Delta storage, large free-text corpora for NLP feature generation, and binary media assets streamed through Kafka into a corporate data lake.
- SQL and data modeling: advanced SQL (window functions, CTEs, execution-plan tuning), relational schema design, dimensional modeling — star schemas, grain declaration, fact table types, SCD (Slowly Changing Dimension) Type 2 — and curated marts serving BI (Business Intelligence) and data-science consumers.
- Reliability and performance: Spark job tuning (partitioning, skew handling, broadcast joins, small-file compaction), pipeline monitoring and alerting, SLA-bound production support and on-call ownership on regulated and peak-load systems.
- Governance and security: auditability and lineage for every derived field, PII (Personally Identifiable Information) handling and masking, access control, and change management inside a banking compliance regime.
Work Experience
Senior Data Engineer - EPAM Systems
UBS (global investment bank), Compliance analytics platform)
Duration: Oct 2025 – Jun 2026
Summary: Bank-wide e-mail and instant-messaging metadata transformed into conversation-level features consumed by finance analytics teams in a strictly regulated environment with full auditability, change control and data-quality gates.
Responsibilities:
- Re-architected legacy shell/SQL/Python batch ETL into Databricks (PySpark) pipelines over a bronze/silver/gold Delta Lake design focusing on lineage, observability and testability.
- Built curated/gold layer datasets by joining pipeline output with Oracle reference data for downstream reporting and modelling.
- Processed weekly batches of millions of communications metadata records with validation and reconciliation checks for banking audit trail.
- Migrated Autosys-orchestrated pipeline estate from RHEL 7 to RHEL 8 with controlled cutover and no disruption.
- Collaborated with analytics and compliance stakeholders to translate business requirements into pipeline logic and data contracts.
Technologies: Azure Databricks, Delta Lake, Apache Spark (PySpark), Python, SQL, Oracle, Bash, Autosys, RHEL 7/8, Git
Senior Software Engineer - EPAM, insurance client (Customer portal backend)
Duration: Mar 2025 – Sep 2025
Summary: Development of backend REST services and third-party system integrations for a customer portal in the insurance domain.
Responsibilities:
- Developed REST services and integrated third-party systems using Java and Spring Boot.
Technologies: Java, Spring Boot
Senior Software Engineer - Hitachi and Disney Advertising (AdTools microservice platform)
Duration: Nov 2023 – Oct 2024
Summary: Microservice platform serving ads across Disney's linear, digital and streaming properties integrating external providers such as Google Ads.
Responsibilities:
- Built event-driven services ingesting ad-delivery event streams via Amazon Kinesis for reporting and revenue reconciliation.
- Developed and integrated microservices on Kubernetes/Istio and implemented Google Ads integration.
- Managed infrastructure with Terraform and ensured code quality through reviews, refactoring, and integration test coverage.
- Monitored production observability using Datadog and Zipkin.
Technologies: AWS (Kinesis, DynamoDB), Java 11–17, Spring Boot, Kubernetes, Istio, Docker, Terraform, Datadog
Senior Software Engineer - client: Tesco (Legacy microservices migration)
Duration: Dec 2022 – May 2023
Summary: Migration of legacy microservices to a reactive stack for one of the world's largest grocery retailers.
Responsibilities:
- Migrated legacy microservices to Spring WebFlux reactive stack.
- Maintained MongoDB-backed services and performed integration testing.
Technologies: Spring WebFlux, MongoDB
Senior Software Engineer - Evri (Hermes),Parcel-logistics microservices
Duration: Jan 2021 – Dec 2022
Summary: Development and support of high-throughput parcel-logistics microservices on AWS ECS, handling peak-season production loads.
Responsibilities:
- Built and supported microservices using Spring Boot, RabbitMQ, Cassandra, and Redis.
- Provided production support under peak-season load conditions.
Technologies: Spring Boot, RabbitMQ, Cassandra, Redis, AWS ECS
Senior Software Engineer - Refinitiv (LSEG) , Content Source Directory
Duration: May 2020 – Dec 2020
Summary: Self-service management platform for news and content sources for a global financial market-data provider.
Responsibilities:
- Designed and implemented serverless services on AWS Lambda with SQS/SNS messaging.
- Contributed to component architecture design and coordinated team work.
Technologies: AWS Lambda, SQS/SNS, Java 11, MySQL
Senior Software Engineer (Data/ML pipelines) - Refinitiv (LSEG), NLP data pipelines for financial news and content intelligence.
Duration: Dec 2018 – Mar 2020
Summary: Development of NLP data pipelines over large text corpora to support financial news and content intelligence using AWS cloud services.
Responsibilities:
- Migrated on-premise data processing workloads to AWS Lambda and Step Functions with infrastructure as code using CloudFormation.
- Built EMR Spark jobs for preparing and transforming large unstructured text corpora for model training.
- Replaced Gensim Word2Vec implementation with Amazon SageMaker.
Technologies: AWS EMR, SageMaker, Lambda, Step Functions, CloudFormation, S3, Java 11, Python 3
Senior Software Engineer - Adidas (Kafka-decoupled microservices for media asset ingestion)
Duration: 2018
Summary: Built microservices decoupled by Kafka to ingest and store media assets and associated metadata into the corporate data lake.
Responsibilities:
- Developed Kafka-based microservices for large-scale distributed file system ingestion and storage of media assets and metadata.
Technologies: Kafka
Senior Software Engineer / Team Lead. Schad (Industrial IoT), Predictive and reactive maintenance analytics
Duration: Nov 2016 – Dec 2017
Summary: Led development of predictive and reactive maintenance analytics for industrial facilities using stream and batch processing of sensor data.
Responsibilities:
- Led a team of 4 engineers building analytics solutions using Apache Spark and Apache Storm.
- Handled stream and batch processing of sensor data for industrial IoT applications.
Technologies: Apache Spark, Apache Storm, Java, Spring, CouchDB, MS SQL Server
Senior Java Developer / Team Lead - easyfinancial (Canada) (Online consumer-lending platform)
Duration: Sep 2013 – Oct 2016
Summary: Development and leadership on a regulated online consumer-lending platform including prequalification, credit adjudication, document verification, e-signing and payments.
Responsibilities:
- Led a team of 4 engineers on a regulated financial product.
- Owned production monitoring and 24/7 support.
- Developed server-side lending workflow functionality using Spring and PostgreSQL.
Technologies: Java, Spring, PostgreSQL
Education
- Lviv Polytechnic National University
B.Sc., Computer Systems Automation and Metrology
2004 – 2008
Upstaffer since August, 2026
