We're currently hiring female candidates for the role based at Bangalore.

Required Skills :

- 8+ years of hands-on experience in data engineering, with at least 4 years in a lead or architect-level role.

- Deep expertise in Apache Spark, with proven experience developing large-scale distributed data processing pipelines.

- Strong experience with Databricks platform and its internal ecosystem (e.g., Delta Lake, Unity Catalog, MLflow, Job orchestration, Workspaces, Clusters, Lakehouse architecture).

- Extensive experience with workflow orchestration using Apache Airflow.

- Proficiency in both SQL and NoSQL databases (e.g., Postgres, DynamoDB, MongoDB, Cassandra) with a deep understanding of schema design, query tuning, and data partitioning.

- Proven background in building data warehouse/data mart architectures using AWS services like Redshift, Athena, Glue, Lambda, DMS, and S3.

- Strong programming and scripting ability in Python (preferred) or other AWS-compatible languages.

- Solid understanding of data modeling techniques, versioned datasets, and performance tuning strategies.

- Hands-on experience implementing data governance, lineage tracking, data cataloging, and compliance frameworks (GDPR, HIPAA, etc.).

- Experience with real-time data streaming using tools like Kafka, Kinesis, or Flink.

- Working knowledge of MLOps tooling and workflows, including automated model deployment, monitoring, and ML pipeline orchestration.

- Familiarity with MLflow, Feature Store, and Databricks-native ML tooling is a plus.

- Strong grasp of CI/CD for data and ML pipelines, automated testing, and infrastructure-as-code (Terraform, CDK, etc.).

- Excellent communication, leadership, and mentoring skills with a collaborative mindset and the ability to influence across functions.