Sr. Data Engineer

At Fabrikatör
, we’re building the next generation of operational intelligence for commerce brands
.

E-commerce teams still run their operations with spreadsheets, gut feeling, and disconnected tools. Forecasts break, stock runs out, money gets locked in inventory, and teams spend their days firefighting instead of building.

We’re fixing this.

Today, Fabrikatör helps brands plan inventory, manage supply, and make better decisions with real data.

Next, we’re going much further:

• An AI-powered Head of Operations that understands demand, supply, and constraints
• A system that lets planners simulate scenarios
  , compare outcomes, and get AI-assisted recommendations
• A platform that turns messy commerce data into clear, explainable decisions
  , not black-box predictions
• Software built for trust, clarity, and long-term thinking
  , not short-term hacks

You’ll be joining while the product is evolving fast, decisions are still being made, and your work will directly shape what thousands of operators rely on daily
.

We are looking for a Senior Data Engineer to join our team and help build and extend our production e-commerce analytics platform. You’ll be working on a system that processes data from multiple sales channels (Shopify, Amazon) and delivers business‑ready insights.

• Extending Analytics
  :
  Build new mart tables and intermediate models for evolving business requirements
• Data Quality
  :
  Debug quality check failures, investigate data discrepancies, ensure accuracy across channels
• Performance Optimization
  :
  Tune Big Query queries, optimize partitioning and clustering strategies
• Customer‑Specific Work
  :
  Develop tenant‑specific analytics and custom reporting
• Cross‑Channel Integration
  :
  Handle the complexity of joining data from Shopify, Amazon, and other platforms with different schemas and update patterns
• ML Pipeline Integration
  :
  Work with forecasting outputs and help integrate ML predictions into analytics workflows
• Documentation
  :
  Maintain semantic layer definitions, update data models, and document patterns

• Data Warehouse:
  Big Query, DuckDB
• Data Ingestion:
  Airbyte
• Data Sources:
  Shopify, Amazon

• Developing new user‑facing features using React and Rails
• Developing a product that users love. Most of the assignments will require you to show strong teamwork with a sense of accountability.
• Consuming our REST API thru our auto‑generated OpenAPI client
• Building reusable components and front‑end libraries for future use
• Translating designs and wireframes into high‑quality code
• Leverage AI in development, write PRDs and feed into AI
• Learn new technologies, improve the tech stack

• Strong SQL skills
  :
  You’ll spend most of your time writing and optimizing complex SQL for analytical workloads
• Big Query or similar experience
  :
  Deep understanding of columnar databases, partitioning, clustering, and cost optimization
• E‑commerce domain knowledge
  :
  Understanding of orders, fulfillments, inventory, returns, and multi‑channel retail
• Data quality mindset
  :
  Experience implementing and debugging data validation, handling edge cases, and ensuring accuracy
• Python proficiency
  :
  For data processing scripts and working with ML outputs
• Incremental processing patterns
  :
  Understanding of how to handle late‑arriving data, updates, and idempotent transformations

• Experience with Bruin, dbt, or similar transformation frameworks
• Hands‑on experience with Airbyte or other ELT tools
• Background in multi‑tenant data architectures
• Exposure to demand forecasting or time‑series analytics
• Experience working with Shopify or Amazon data APIs/schemas

• Pragmatic problem‑solving
  :
  Our platform is in production—changes need to be thoughtful and tested
• Attention to detail
  :
  Data accuracy matters. A small bug can propagate to customer dashboards
• Clear communication
  :
  You’ll work with product, engineering, and AI teams
• Curiosity
  :
  The e‑commerce data landscape is complex and always evolving
• Your experience with similar data platforms (e‑commerce analytics, multi‑channel data, Big Query/columnar databases)
• A brief overview of a challenging data engineering problem you’ve solved

We’re looking for someone who can hit the ground running and contribute to a production system from day one.