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Metro Bangalore, common Data Engineer title, and broad tooling cause moderate competition.
Core data engineering skills are broadly transferable across industries despite domain-specific sensor experience.
Explicit 7–11 years plus mandatory streaming, lakehouse, orchestration, and infra skills imply high strictness.
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Design, implement, and maintain scalable real-time and batch data pipelines using Kafka, Spark, Flink, and Airflow for high-throughput ingestion and processing.
Manage data lakes and warehouses (e.g., Iceberg, Delta Lake) to support analytics and ML workloads including provisioning ML-ready datasets.
Ensure pipeline scalability, observability, data quality, and participate in deploying data infrastructure across on-prem and hybrid environments including automation with Terraform/Ansible.
7–11 years total experience with at least 2 years specifically in real-time or streaming data pipelines.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Proficiency in Python or Java, SQL, and experience with Apache Kafka, Apache Spark or Flink for real-time and batch processing.
Onsite work requirement in Indira Nagar, Bangalore (4 days/week).
Experienced with managing hybrid and on-premise data infrastructure and deployments alongside cloud platforms (AWS/GCP/Azure).
Strong background in data modeling, schema evolution, data quality, observability, and orchestration tools like Airflow or dbt.
Comfortable collaborating closely with AI/ML engineers and DevOps teams to optimize data flows for analytic and ML applications.