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# Data Engineer

- **Company:** K1X, Inc.
- **Location:** Indianapolis, IN, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Business Analytics Applications, Microsoft Azure, Big Data, Data Validation, Information Engineering, Extract Transform Load (ETL), Data Systems, Relational Databases, Machine Learning, Microsoft SQL Server, Power BI, SQL Databases, Cloud Platform System, Snowflake, Data Management, Machine Learning Operations, Data Lakehouse, Data Pipelines
- **Published:** August 9, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=af76b5bf1a287eca

## About the Role

Who You Are

 * 4-8+ years of experience in data engineering or backend engineering with a data focus.
 * Strong experience building data pipelines and ETL/ELT workflows.
 * Proficiency with SQL and relational data modeling.
 * Experience with cloud-based data platforms (Azure preferred).
 * Understanding of data quality, reliability, and monitoring.
 * Strong communication and collaboration skills.

It's Truly a Match If You Have

 * An engineering mindset applied to data platforms.
 * Passion for building scalable, reliable data foundations.
 * Interest in enabling ML and analytics-driven products.
 * Experience or familiarity with Snowflake, SQL Server, Azure, and Power BI.
 * Comfort balancing near-term delivery with long-term architecture.

## Description

We are seeking a Data Engineer to design, build, and maintain the data pipelines and infrastructure that power K1x's machine learning initiatives and future analytics-driven product features.

This role sits at the intersection of Product Engineering, Data, and Machine Learning. You will be responsible for creating reliable, scalable, and high-quality data systems that enable downstream ML models, reporting, and embedded analytics.

What You'll Do

 * Design, build, and maintain scalable data architectures supporting operational, analytical, and ML workloads.
 * Contribute to the evolution of K1x's data lakehouse strategy, including adoption of Snowflake.
 * Develop and maintain robust ETL/ELT pipelines for large-scale data processing.
 * Implement data quality checks, validation, and monitoring.
 * Enable machine learning workflows with clean, well-structured datasets.
 * Support analytics and reporting use cases including Power BI.
 * Partner with Product, Engineering, and AI teams on data solutions.


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