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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 it 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. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.