Data Engineer (3–6 Years)

We are looking for a Data Engineer who can work across modern data platforms and streaming frameworks to build scalable and reliable pipelines. If you enjoy working with Spark on Databricks, Kafka, Snowflake, and MongoDB — and want to solve real-world data integration challenges — this role is for you.

What you’ll do:

• You will develop ETL/ELT pipelines in Databricks (PySpark notebooks) or Snowflake (SQL/Snowpark), ingesting from sources like Confluent Kafka

• Handle data storage optimizations using Delta Lake/Iceberg formats, ensuring reliability (e.g., time travel for auditing in fintech pipelines).

• Integrate with Azure ecosystems (e.g., Fabric for warehousing, Event Hubs for streaming), supporting BI/ML teams—e.g., preparing features for demand forecasting models

• Contribute to real-world use cases, such as building dashboards for healthcare outcomes or optimizing logistics routes with aggregated IoT data.

• Write clean, maintainable code in Python or Scala

• Collaborate with analysts, engineers, and product teams to translate data needs into scalable solutions

• Ensure data quality, reliability, and observability across the pipelines
  
  

What we’re looking for:

• 3–6 years of hands-on experience in data engineering
  
  

• Experience with Databricks / Apache Spark for large-scale data processing
  
  

• Familiarity with Kafka, Kafka Connect, and streaming data use cases
  
  

• Proficiency in Snowflake — including ELT design, performance tuning, and query optimization
  
  

• Exposure to MongoDB and working with flexible document-based schemas
  
  

• Strong programming skills in Python or Scala
  
  

• Comfort with CI/CD pipelines, data testing, and monitoring tools
  
  

Good to have: -

• Experience with Airflow, dbt, or similar orchestration tools
  
  

• Worked on cloud-native stacks (AWS, GCP, or Azure)
  
  

• Contributed to data governance and access control practices