Data Engineer (3 - 4 Years)
Responsibilities
- Watch Reels and YouTube videos every day level up on your fashion sense
- This is a hands-on engineering role where you will actively build and write production code - not just manage or delegate.
- Design, build, and maintain scalable batch and near real-time data pipelines.
- Develop ETL/ELT pipelines using Python, SQL, and Spark/PySpark on Databricks.
- Build Bronze, Silver, and Gold data models using a Lakehouse (Medallion) architecture.
- Optimize Spark jobs for performance, scalability, and cost.
- Work with structured and semi-structured datasets from multiple sources.
- Develop reusable, production-ready data engineering frameworks.
- Own data quality, monitoring, and pipeline reliability as measurable outcomes.
- Collaborate closely with Product, Analytics, and Business teams.
- Participate in code reviews and production support.
- Navigate our current stack: Spark, Databricks, Delta Lake (Medallion architecture), BigQuery, Airbyte, and Power BI.
Requirements




- If you are a content creator yourself or have worked closely in the social media space, we like you already :)
- 3+ years of professional experience in Data Engineering, with a strong software engineering foundation.
- Robust SQL skills (joins, CTEs, window functions, query optimization).
- Hands-on Python programming skills.
- Hands-on experience with Apache Spark / PySpark / Databricks.
- Solid knowledge of data warehousing and data modeling.
- Experience with ETL/ELT and incremental data processing.
- Experience with the AWS or GCP cloud ecosystem.
- Strong communication skills - you can translate between data engineering and business/analytics stakeholders.
Good to Have
- Airbyte, Kafka, or CDC concepts.
- Delta Lake or Apache Iceberg.
- Airflow or Databricks Workflows.
- Docker / Kubernetes.
- Performance tuning and partitioning strategies.

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