Data Engineer

Company DescriptionRelativix is a vehicle intelligence platform that connects fleet operators and repair shops through shared, high-resolution diagnostic data. The company continuously ingests detailed vehicle information from internal CAN networks and uses predictive analytics to provide clear, evidence-based root-cause insights before vehicles reach the shop. This approach reduces diagnostic guesswork, shortens repair cycles, and improves technician productivity by supplying full historical context instead of isolated error codes. Built as a hardware-enabled, cloud-native platform, Relativix supports modern integrations and agentic AI workflows. The platform helps repair shops work faster and smarter while enabling fleets to minimize downtime, lower maintenance costs, and keep vehicles on the road.Role DescriptionThis is a full-time remote role for a Data Engineer. The Data Engineer will design, build, and maintain large-scale data pipelines on the Databricks platform that ingest vehicle telemetry and diagnostic data from diverse sources into Relativix's cloud-native platform on AWS. Day-to-day responsibilities include implementing and optimizing ETL processes at scale, developing and refining data models, and supporting data warehousing and lakehouse solutions to ensure high-quality, reliable datasets for analytics, machine learning, and AI workflows. The role involves collaborating closely with software engineers, data scientists, and product teams to deliver performant data infrastructure, prepare and serve data for machine learning models, and enable advanced predictive capabilities. The Data Engineer will also contribute to monitoring, troubleshooting, and continuously improving data systems to support fleet and repair shop customers.Who You AreWe are looking for someone who is passionate about early-stage startups and wants true ownership of what they build. You will work directly with the leadership team to shape and ship cutting-edge products, not execute someone else's spec. Relativix is a flat organization where everyone, including leadership, is a hands-on individual technical contributor. If you want your work to define the foundation of the product and the company, this role is for you.CompensationThis position is equity-based to start. Once our current funding round closes, it will convert to a salaried position with competitive compensation. This is an opportunity to join at the ground floor with meaningful ownership in the company.QualificationsSolid, hands-on understanding of the Databricks platform, including Spark, Delta Lake, and lakehouse architecture for large-scale data processing.Proven experience designing, running, and optimizing large-scale ETL pipelines, including data ingestion, transformation, and integration from multiple sources.Strong data engineering fundamentals, including building and maintaining scalable data pipelines and distributed data systems.Solid understanding of AWS and its data services (e.g., S3, Glue, Kinesis, Redshift, Lambda) for building production data infrastructure.Good understanding of machine learning models and their data requirements, including feature engineering, training/serving data pipelines, and supporting data scientists in deploying models to production.Experience with data modeling and data warehousing to design robust schemas and storage solutions for analytics and reporting.Proficiency in a modern programming language commonly used for data engineering (e.g., Python, Scala, or Java) and strong SQL skills.Understanding of best practices in data quality, data governance, and security for production environments.Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.A self-starter mentality with the drive to own projects end-to-end and thrive in the ambiguity of an early-stage startup.Ability to work independently in a remote environment, communicate clearly with cross-functional teams, and manage priorities in a fast-paced setting.Experience with IoT data manipulation, vehicle telemetry, automotive data, or real-time streaming analytics (e.g., Kafka, Kinesis, Spark Structured Streaming) is a strong plus.