Software Engineer II

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe.

Designs and builds robust, scalable data pipelines and data platform solutions across Tekion's enterprise data platform. Works independently on complex data engineering features and systems while collaborating with peers to deliver high-quality, AI-assisted data products, optimize pipeline performance, and continuously improve data engineering practices. Brings a strong eye for data quality and builds pipelines that are end-to-end reconcilable by design.

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  Designs, develops, and maintains data pipelines, data models, and data platform services across the Bronze, Silver, and Gold layers.

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  Writes clean, efficient, and well-documented transformation logic using dbt and Databricks Delta Live Tables (DLT), with a focus on scalability, maintainability, and data correctness.

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  Participates in data architecture discussions and contributes to decisions around pipeline design, data modeling, and platform component design.

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  Builds and enforces data quality checks at every layer of the pipeline, including schema validation, null checks, referential integrity, and business rule assertions, ensuring issues are caught early and never silently passed downstream.

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  Designs pipelines with end-to-end reconciliation in mind, ensuring row counts, aggregates, and key metrics can be traced and validated from source systems through to Gold layer outputs.

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  Monitors and responds to data quality alerts, investigates anomalies, performs root cause analysis, and drives permanent fixes rather than temporary patches.

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  Develops and maintains data quality SLAs and works with upstream data owners to resolve source-level issues that impact downstream data products.

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  Develops and maintains data quality checks, pipeline tests, and end-to-end validation to ensure data product reliability and accuracy.

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  Collaborates with cross-functional teams including business stakeholders, analysts, and product managers to gather requirements and translate them into well-defined data products.

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  Troubleshoots production pipeline failures and supports root cause analysis to drive platform reliability and SLA adherence.

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  Builds and maintains analytical dashboards using Tableau or Databricks AI/BI, enabling self-serve insights for business teams.

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  Designs, builds, and optimizes Databricks Genie spaces to support natural language querying of Gold layer data.

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  Develops and optimizes AI agents and data agents, leveraging AI coding assistants to accelerate pipeline development and reduce time to delivery.

• 4 to 6 years of experience in data engineering or a closely related discipline

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field

• Hands-on experience building and operating data pipelines on Databricks or an equivalent AI-first data platform

• Proficiency in Python and SQL; experience with dbt and Databricks Delta Live Tables (DLT) for real-time and near-real-time pipeline development

• Experience designing and implementing data quality frameworks including schema validation, null and duplicate checks, referential integrity enforcement, and business rule assertions

• Demonstrated ability to build end-to-end reconcilable pipelines where row counts, aggregates, and key metrics can be validated at every stage from source to Gold

• Experience with relational and/or NoSQL databases, REST APIs, and cloud-based data services

• Strong understanding of lakehouse architecture, data modeling, testing practices, and CI/CD pipelines for data workflows

• Demonstrated ability to build and optimize Databricks Genie spaces and AI/BI dashboards for business consumption

• Experience building, deploying, and optimizing AI agents, with the ability to use AI coding agents efficiently to accelerate development

• Ability to understand business requirements, engage confidently with stakeholders, and translate those requirements into reliable data products

• Strong communication and collaboration skills across both technical and non-technical audiences