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Mid-level metro data role with broad skills and common experience band increases competitiveness.
Core data engineering skills are broadly transferable, though specific cloud/Databricks expertise raises domain sensitivity.
Explicit 2-4 years plus broad mandatory cloud and data-platform tech increases filtering strictness.
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Build and automate end-to-end ETL/ELT pipelines using Azure Data Factory, AWS Glue, and Apache Airflow.
Develop large-scale distributed data processing jobs using PySpark and Scala in Databricks or EMR environments.
Design real-time data ingestion systems with Apache Kafka, and manage cloud data storage optimizing for ACID transactions using Delta Lake or Apache Iceberg.
2-4 years of professional experience in data engineering, backend engineering, or related field.
Bachelor’s degree in Engineering.
Strong hands-on experience with Azure (ADF, Synapse, Databricks) and AWS (S3, Glue, Athena, Lambda).
Proficiency in Python (PySpark, FastAPI), SQL; familiarity with Java or Scala mandatory.
Experienced in building and optimizing data pipelines with distributed computing frameworks and real-time data processing.
Skilled in cloud-native data infrastructure deployment and management using Terraform, CloudFormation, Docker, Kubernetes.
Comfortable working in Agile, fast-paced environments with ability to translate technical details for non-technical stakeholders.