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Mid-level, broadly-applicable data engineering role with common tech stack and metro location, moderate competition.
Data engineering skills are moderately transferable, but cloud/Databricks specialization increases domain sensitivity.
Explicit 6–8 years plus many mandatory tools (Databricks, PySpark, Airflow, Kafka, Terraform) makes filtering strict.
Design and implement scalable data platforms using Data Lake, Lakehouse, and Data Mesh architectures.
Build and optimize batch and real-time data pipelines with tools like Databricks, Apache Spark, Kafka/Kinesis, and AWS services.
Develop Python-based microservices, RESTful APIs, and event-driven services to enable secure and efficient data sharing and processing.
6–8 years of experience in Data Engineering or related roles.
Strong proficiency in Python, PySpark, and SQL.
Hands-on experience with Databricks, Delta Lake, Snowflake, Apache Airflow, Kafka or AWS Kinesis, and AWS cloud services (S3, Glue, Athena, Redshift).
Experience with Infrastructure as Code (Terraform), Docker, and data quality/observability tooling.
Experienced in modern data architectures including Data Lakehouse, Data Mesh, and Data Products.
Skilled in building scalable batch and streaming data pipelines in cloud-native environments.
Proficient in data modeling (Star Schema, Snowflake Schema, 3NF) and implementing data orchestration and serverless processing solutions.