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Mid-level experience, metro Bengaluru location, and common data-engineer profile increase competitive density.
Core data engineering skills transfer across industries, though Databricks and SIEM specialization increases specificity.
Explicit 5.5+ years plus mandatory Databricks, Spark, Kafka and Terraform creates strict filtering.
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Design, develop, and maintain scalable batch, near real-time, and streaming data pipelines using Databricks, Apache Spark, and Python.
Build and operationalize end-to-end data ingestion and transformation pipelines, supporting Raw, Trusted, and Curated data architectures on cloud platforms like Azure Databricks.
Automate infrastructure provisioning and deployment using Terraform, GitHub Actions, and CI/CD pipelines; collaborate with cross-functional teams to ensure high data quality, governance, and platform reliability.
5.5+ years total work experience in data engineering or related roles.
Strong hands-on experience with Python, SQL, Databricks, Apache Spark, Apache Kafka, and Terraform.
Proven experience building scalable batch and real-time data pipelines on Azure Databricks or similar cloud platforms.
Bachelor’s or master’s degree in Computer Science, Information Technology, or related field.
Experienced in developing and optimizing metadata-driven ETL/ELT pipelines using modern data engineering best practices and tools like dbt, Airflow, and Databricks Workflows.
Comfortable working within Agile and DevOps environments, with knowledge of CI/CD, Git, and Infrastructure as Code.
Capable of mentoring junior engineers and driving adoption of new cloud and AI/ML innovations in a collaborative team setting.