About this role
Tekion's enterprise data platform is what feeds analytics, operational reporting and AI features for thousands of car dealerships. This role builds the pipelines behind it, working through bronze, silver and gold layers with dbt and Databricks Delta Live Tables. What sets the posting apart is how much of it is about correctness rather than throughput: it asks you to build data quality checks at every layer, design pipelines that reconcile end to end so row counts and key metrics can be traced from source to output, and fix root causes instead of patching. If you have done data engineering where nobody checked whether the numbers actually tied out, this is the opposite of that. You would also build Tableau or Databricks dashboards and Genie spaces for natural language querying.
Who this is for
Asks for 4 to 6 years in data engineering or a closely related discipline and a Bachelor's degree in Computer Science, Engineering, Data Science or a related field. Required: hands on experience building and operating data pipelines on Databricks or an equivalent AI first data platform, and proficiency in Python and SQL.
Day to day: design, develop and maintain data pipelines, data models and platform services across the bronze, silver and gold layers; write transformation logic in dbt and Databricks Delta Live Tables; contribute to data architecture discussions on pipeline design and data modelling; build and enforce data quality checks at every layer including schema validation, null checks, referential integrity and business rule assertions so problems are caught early rather than passed downstream; design pipelines for end to end reconciliation so row counts, aggregates and key metrics can be traced and validated from source systems to gold layer outputs; monitor and respond to data quality alerts, investigate anomalies and drive permanent fixes; maintain data quality SLAs and work with upstream data owners on source level issues; troubleshoot production pipeline failures; build analytical dashboards in Tableau or Databricks AI/BI for self serve use; and build and optimise Databricks Genie spaces for natural language querying of gold layer data.
The posting also mentions using AI coding assistants to speed up pipeline development.
Location is Bangalore HQ. No office day count and no interview process are published.
Fit note: the emphasis on reconciliation and data quality SLAs is unusual and specific. Expect interview questions about how you prove a pipeline is correct, not just that it ran.
Day to day: design, develop and maintain data pipelines, data models and platform services across the bronze, silver and gold layers; write transformation logic in dbt and Databricks Delta Live Tables; contribute to data architecture discussions on pipeline design and data modelling; build and enforce data quality checks at every layer including schema validation, null checks, referential integrity and business rule assertions so problems are caught early rather than passed downstream; design pipelines for end to end reconciliation so row counts, aggregates and key metrics can be traced and validated from source systems to gold layer outputs; monitor and respond to data quality alerts, investigate anomalies and drive permanent fixes; maintain data quality SLAs and work with upstream data owners on source level issues; troubleshoot production pipeline failures; build analytical dashboards in Tableau or Databricks AI/BI for self serve use; and build and optimise Databricks Genie spaces for natural language querying of gold layer data.
The posting also mentions using AI coding assistants to speed up pipeline development.
Location is Bangalore HQ. No office day count and no interview process are published.
Fit note: the emphasis on reconciliation and data quality SLAs is unusual and specific. Expect interview questions about how you prove a pipeline is correct, not just that it ran.
Apply on company site
Opens jobs.ashbyhq.com, the employer's own application page. Applying is always free.