Office hour: Monday – Friday (9:00 AM – 6:00 PM) 

• Build and operate batch and near–real-time data pipelines integrating POS/ERP/WMS systems, e-commerce, marketing platforms, and external data sources.
• Orchestrate and monitor workflows using Airflow, Prefect, or Dagster, ensuring defined SLAs and reliable incident management.
• Design and maintain scalable schemas and core domain data models (Products, SKUs, Inventory, Sales, Suppliers, Warehouses).
• Implement data quality frameworks including validation, reconciliation, freshness checks, and observability.
• Develop, optimize, and maintain cloud data warehouses (BigQuery, Snowflake, or Redshift) with efficient dbt modeling.
• Collaborate with ML/DS teams to deliver feature stores, training datasets, and production-ready model pipelines.
• Establish and enforce data governance, security standards, documentation practices, and reusable data contracts/APIs.

• 4-6+ years of experience as a Data Engineer.
• Strong SQL and solid Python skills (pandas/PySpark is a plus).
• Hands-on experience with modern data warehouses: BigQuery, Snowflake, or Redshift.
• Experience building production ETL/ELT pipelines using Airflow, Prefect, Dagster, dbt, or similar tools.
• Strong understanding of data modeling (star/snowflake schemas, SCD, event modeling).
• Experience with cloud platforms (GCP/AWS/Azure) and basic infra (Docker, CI/CD).
• Good instincts for data quality, observability, and distributed system failure modes.
• Clear communication and ability to collaborate across technical and non-technical teams.
• Nice to have:
  • Experience with retail/fashion/e-commerce data: inventory, sales, returns, promotions.
  • Exposure to forecasting, time-series modeling, or ML pipelines.
  • Startup experience, especially in early-stage environments.
  • Familiarity with modern ELT stacks: Fivetran, Airbyte, or custom connectors.
  • Experience building data products for analytics dashboards or embedded BI.