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Niche technical requirements reduce competition, but known startup brand increases applicant volume to medium.
Core data engineering skills are easily transferable across industries despite automotive domain focus.
Explicit 6+ years plus many mandatory technologies imply strict shortlisting.
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Design and evolve a multi-tenant, cloud-scale data platform for thousands of dealerships and business users with strong tenant isolation and data governance.
Drive transition from batch to near real-time and real-time data ingestion architectures enabling faster decision-making and AI/ML use cases.
Build and maintain scalable data platforms, warehouses, and lakehouse architectures supporting analytics and AI across the enterprise.
6+ years of experience in Data Engineering.
Strong proficiency in Python, SQL, and Apache Spark.
Hands-on experience with AWS data services (EMR, S3, Glue, Athena) and streaming technologies (Kafka, Flink, or Kinesis).
Expertise in data modeling (dimensional modeling, Data Vault), lakehouse technologies (Delta Lake, Apache Iceberg, Apache Hudi), and workflow orchestration (Airflow).
Experienced in designing and operating large-scale, multi-tenant, cloud-based data platforms with a focus on tenant isolation and data governance.
Proficient in building both batch and real-time ETL/ELT pipelines supporting complex analytics and AI use cases.
Skilled in advanced SQL query optimization, distributed processing frameworks, and data quality/monitoring frameworks.