Senior Software Engineer - Data Engineering

A serious data engineering role rather than a pipeline maintenance one. Tekion is moving its data platform from batch processing to near real time and real time ingestion, and this seat helps drive that. It is a multi tenant, cloud scale platform serving thousands of dealerships, so tenant isolation, governance and data quality are constraints rather than nice to haves. The requirement list is specific and modern: Python, SQL and Apache Spark, batch and streaming ETL and ELT, AWS services including EMR, S3, Glue and Athena, Kafka, Flink or Kinesis for streaming, dimensional modelling and Data Vault, lakehouse formats such as Delta Lake, Iceberg or Hudi, and Airflow for orchestration. It also asks for data quality frameworks and an understanding of partitioning, schema evolution and performance tuning. 6+ years, Bengaluru.

Required by the posting: 6+ years of experience in Data Engineering. Strong expertise in Python, SQL and Apache Spark. Experience building scalable batch and real time ETL and ELT pipelines. Hands on experience with AWS services including EMR, S3, Glue and Athena. Experience with Kafka, Flink or Kinesis for streaming data processing. Strong knowledge of dimensional modelling, Data Vault and data warehousing concepts. Experience with Delta Lake, Apache Iceberg or Apache Hudi. Expertise in workflow orchestration using Airflow. Experience implementing data quality frameworks and monitoring solutions. Strong understanding of partitioning, schema evolution and performance optimisation. Familiarity with CI/CD, Git and infrastructure as code tools is listed as a plus rather than a requirement.

The real day to day: designing and building scalable data platforms, data warehouses and lakehouse architectures; data modelling including dimensional modelling, Data Vault and enterprise data architecture; advanced SQL with query optimisation, performance tuning and large scale processing; distributed processing with Spark; designing batch, streaming and change data capture ingestion pipelines; and helping drive the platform's evolution from batch oriented processing to near real time and real time ingestion.

Platform context from the posting: a multi tenant, cloud scale data platform serving thousands of dealerships and business users while maintaining tenant isolation, governance and data quality standards, feeding analytical products, operational insights and AI driven experiences.

Location: Bengaluru, Karnataka.

Benefits named on Tekion's postings: competitive compensation, generous stock options, and medical insurance coverage.

Honest fit guidance: this requirement list is long but coherent, and it describes a modern lakehouse stack rather than a legacy warehouse one. If your data engineering has been mostly batch SQL and scheduled jobs, the streaming half (Kafka, Flink or Kinesis, plus change data capture) is the gap that will show. The multi tenancy requirement is the genuinely hard part and is rarer on a CV than Spark is.