Marcin – SQL, AWS, Python

User management platform for Poland’s largest football organizations, used to collect, store, and manage fanbase data at scale (more than 600k active users). The system centralizes profiles, preferences, and engagement data, and exposes APIs to internal tools and partner applications. It is built as an event-driven backend optimized for scalability, security, and data privacy.

• Designed backend architecture and data models for a multi-tenant, event-driven user management platform;
• Built REST and event-based APIs using AWS Lambda, API Gateway, Cognito, SQS/SNS/EventBridge, RDS (PostgreSQL) as the main data store, and S3 as data lake;
• Automated infrastructure provisioning with the Serverless Framework, covering services, queues, and database resources;
• Enforced data privacy, auditability, and compliance across all storage and processing layers;
• Set up CI/CD pipelines (CircleCI) for automated testing and deployment of backend services;
• Improved service reliability through strong typing, static analysis, smoke testing, and upgrades of legacy dependencies.

Cloud-based data platform for a leading pharmaceutical company, powering analytics, reporting, and AI workloads across multiple business units.

• Introduced and championed Apache Airflow for modern data orchestration, implementing deferred execution in Kubernetes to offload thousands of heavy tasks from the scheduler;
• Built an in-house Python library for dbt Cloud account management via Terraform CDK, enabling hundreds of projects to be deployed as code with GitOps workflows, auditability, and enforced company standards;
• Delivered platform-wide improvements used by 100+ developers and analytics engineers, streamlining workflows and unifying engineering practices across the data platform.

Internal “business intelligence operating system” served as a unified reporting platform used across Pitney Bowes to deliver real-time and batch analytics. The system integrates multiple BI tools and microservices into a single environment with shared access control, monitoring, and integrations. It powers both internal and client-facing reporting with strict reliability, performance, and security requirements.

• Developed and maintained an asynchronous Python backend (AIOHTTP) and event-driven microservices on AWS (ECS Fargate, Lambda, SQS, RDS), powering the company’s unified BI operating platform;
• Designed and implemented core platform components, including OIDC authentication, RBAC authorization, nginx reverse proxy, Alembic-based migration framework, and multiple third-party API integrations;
• Built and evolved GitLab CI/CD pipelines for backend services, infrastructure, and BI workflows, significantly improving delivery speed and deployment safety;
• Led major cost-optimization initiatives, reducing AWS spend by ~35% and vendor licensing costs by ~25% through architectural improvements and migration to open-source tooling;
• Improved data freshness for operational dashboards by ~60× (15 min → 15 sec) via architectural redesign and performance tuning of backend services and data flows;
• Established comprehensive monitoring and operational visibility across the stack (CloudWatch, Sentry), strengthening platform reliability and supporting security hardening that resulted in 0 high/critical vulnerabilities during penetration tests;
• Promoted to Engineering Manager; led a remote full-stack team (5 FTE) while remaining 50% hands-on with focus on IaC, cost optimization, DevOps practices, event-driven microservices, performance & tech debt;
• Managed AWS accounts and multiple self-hosted BI tools (Tableau, Metabase, Retool, Appsmith, Dagster, Streamlit), ensuring availability, governance, and cost efficiency;
• Streamlined product proposal documents into actionable engineering requirements, driving successful execution of complex internal initiatives;
• Hired 3 engineers with 0% team churn, embedded strong engineering standards, and mentored team members through code reviews, architectural guidance, and continuous improvement practices.

A global data platform powering analytics, operational reporting, and business intelligence across multiple regions (North America, EMEA, APAC). The platform processes large volumes of operational, marketing, and financial data, providing standardized datasets and automated pipelines used by product, finance, sales, and operations teams company-wide.

• Designed and maintained reliable data pipelines powering critical analytical workflows across global teams;
• Promoted high technical standards within data architecture, delivering robust, high-quality data products aligned with business needs;
• Automated operational processes, significantly reducing manual work and freeing teams to focus on high-value analysis;
• Mentored junior analysts and supported their growth in data engineering best practices.