- 4 to 6+ years of experience in data engineering, data platform, or similar roles.

- Strong hands-on expertise with Databricks : Spark (PySpark / Scala), Delta Lake, job scheduling/workflows, data transformations.

- Proficiency in Python, SQL, and strong understanding of distributed data processing, storage formats, metadata management.

- Experience building/maintaining large-scale data pipelines (batch + streaming).

- Familiarity with cloud platforms (e.g. AWS, Azure, GCP) : storage (S3/ADLS), compute, networking, permissions, and cloud-native data architecture.

- Understanding of data governance, security, access controls, and data compliance considerations.

- Ability to write clean, maintainable, well-documented code; follow best practices, testing, version control

Preferred / Nice-to-Have :

- Experience with data modeling (star schema, snowflake, dimensional modeling), data warehousing or lakehouse design.

- Experience with django and django rest framework or similar api development frameworks.

- Familiarity with data quality frameworks, observability, monitoring, lineage tracking.

- Exposure to ML/AI pipelines, feature stores, real-time analytics, or streaming-first architectures.

- Good communication skills, ability to work with cross-functional teams, mentor others, and handle high-ownership tasks.