Data Engineering Lead
5 days ago
**Key Responsibilities**
- Provide technical leadership for migration projects from Cloudera (Spark, Hive, Kafka, Control-M) to Kubernetes stack (Spark 3.5, DBT, Airflow, MinIO/S3, Kafka, Solace).
- Lead a small team and internal engineers to deliver project deliverables.
- Participate in design, architecture discussions, and migration planning with internal leads.
- Build and review high-performance, production-ready pipelines.
- Ensure adherence to standards, compliance, and governance requirements.
- Provide status reporting, escalations, and delivery tracking to stakeholders.
- Design and implement migration/acceleration framework to automate end to end migration.
- Continuous enhancements to the frameworks to ensure the stability, scalability and support for diverse use cases and scenarios.
**Required Skills**:
- 9-12 years of experience in data engineering and big data ecosystems.
- Strong hands-on expertise in Spark, Hive, Kafka, Solace.
- Working experience with Kubernetes deployments and containerized data workloads.
- Proficiency in Python, Scala, and/or Java.
- Experience in orchestration tools (Airflow, Control-M) and SQL transformation frameworks (DBT preferred).
- Familiarity with object stores (S3, MinIO).
- Hands on experience of data Lakehouse formats (Iceberg, Delta Lake, Hudi).
- Prior experience leading vendor or distributed teams for enterprise projects.
**Engagement Expectations**
- Vendor resource will be expected to ramp up quickly and start contributing to active migration projects.
- Must coordinate with internal leads, product owners, and platform teams to ensure project timelines are met.
- Should be flexible to support multiple time zones (if required).
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