We built a comprehensive data synchronization and warehousing platform for a thriving e-commerce brand specializing in novelty apparel. The system aggregates fragmented data from Shopify, diverse marketing campaign sources, and internal ERP tools into a single, unified reporting layer. Today the e-shop owners use the platform as their primary source of truth for marketing decisions and for planning manufacturing down to exact product variants and volumes.
Core competencies
Technologies
Problem definition and goal
The legacy data warehouse — built on SQL Server and orchestrated by Apache Airflow — had grown into an unmaintainable tangle of hundreds of stored procedures, monolithic pipelines with thousands of interdependent tasks, and no version control. Business users stopped trusting the data after encountering duplicate records, conflicting reports, and inconsistencies discovered in client presentations rather than by the engineering team.
The goal: replace it with a platform where data is trustworthy, traceable, and easy to extend.
A single task failure could silently cascade and block entire pipelines for hours. There was no isolation, no clear ownership, and no way to quickly identify what had gone wrong and why.
Hundreds of stored procedures with overlapping logic and zero version control made every change a gamble.
Duplicate records, missing values, and cross-source metric mismatches were regularly caught by business stakeholders rather than engineers. There was no systematic data quality layer — quality was everyone's concern and no one's responsibility.
Integrating a new data source meant navigating an undocumented codebase with no consistent patterns. What should take days took weeks, and each addition introduced new fragility into existing pipelines.
We rebuilt the platform end to end on a modern data stack — Dagster for orchestration, dbt on PostgreSQL for transformations — with data trustworthiness as the core engineering priority.
Dagster replaced Airflow as the orchestration engine, with pipelines rebuilt as small, clearly scoped jobs. Every data source follows a consistent, documented module structure — making new integrations predictable and fast, without touching existing code.
All SQL transformations were rebuilt in dbt using a strict three-layer architecture: staging, intermediate, and marts. Every model is documented, typed, and stored in Git — giving the team a full audit trail and the ability to review any change before it reaches production.
The platform includes two tiers of automated testing: structural checks such as uniqueness, nulls, and referential integrity, and cross-source metric validation — for example, confirming that hourly campaign spend rolls up correctly to daily totals across every integrated advertising platform. Together with alerting and automated re-runs, quality is enforced by the system, not left to chance.
Standardized connectors handle data from REST APIs, GraphQL APIs, CSV files, and relational databases under a single consistent framework. Adding a new data source is a matter of following a well-defined pattern, not decoding tribal knowledge.
The mart layer is designed with semantic clarity — well-named entities, consistent grain, and clean metric definitions. This makes it directly usable as a knowledge base for large language model tooling: natural language querying, automated report generation, and AI-assisted insight discovery become realistic next steps rather than distant aspirations.
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