Senior Data Engineer

General Motors•Austin, TX

7d•$119,200 - $175,450•Hybrid

About The Position

We're looking for a hands-on Senior Data Engineer to build the next generation of telematics data products that power analytics, artificial intelligence, and decision-making across the enterprise. This role is about creating trusted, reusable, analytics-ready data products that make complex telemetry data easier to discover, understand, and use at scale. You'll help move the organization from custom analytics and duplicated logic toward standardized, reusable data products that deliver consistent value across teams. You'll help define foundational datasets, shared metrics, and curated data products that enable teams across engineering, product, quality, safety, and operations to unlock the value of connected vehicle data. Your work will help establish a trusted, common data foundation for connected vehicle insights. By replacing duplicated analytical logic with reusable, governed data products, you'll improve consistency, increase adoption, and accelerate analytics, machine learning, and AI use cases across the enterprise. You'll make complex telemetry data easier to discover, understand, and use—helping teams move faster from raw signals to confident decisions about product quality, vehicle performance, safety, and operations.

Requirements

Bachelor's degree in computer science, software engineering, data science, or a related field, or equivalent experience.
5+ years of relevant experience in data engineering, software engineering, or a related discipline.
Experience supporting AI or machine learning through feature engineering, model-ready data, experimentation workflows, or model deployment.
Experience building GenAI, retrieval-augmented generation, natural-language analytics, or self-service data tools.
Experience with cloud data services such as Azure Data Lake, Azure Kubernetes Service, AWS EMR, or comparable technologies.
Experience with workflow orchestration, CI/CD, infrastructure as code, automated testing, and data observability.
Strong experience with enterprise data pipelines, data modeling, data integration, and production support.
Proficiency in Python or Scala, advanced SQL, and Unix/Linux.
Hands-on experience with Databricks, Apache Spark, Delta Lake, and distributed data processing.
Experience with relational, key-value, document, or other NoSQL data stores.
Experience designing data solutions on Azure; AWS or GCP experience is also applicable.
Knowledge of batch and streaming architectures, cloud platforms, Kubernetes, and distributed systems.
Experience with data formats such as JSON, Parquet, Avro, and XML.
Understanding of data governance, privacy, security, data quality, and responsible AI practices.
Ability to work independently, solve complex problems, manage competing priorities, and communicate technical concepts clearly.

Nice To Haves

Master's degree in computer science, software engineering, data science, or a related field.
Experience with data catalogs, metadata management, lineage, and governance platforms such as Atlan, Collibra, or Alation.
Demonstrated technical leadership, mentoring, cross-functional collaboration, and process improvement impact.

Responsibilities

Design, build, and productionize secure, scalable batch and streaming data pipelines in Azure Databricks and cloud environments.
Transform data from multiple source systems into trusted, well-structured datasets for analytics, AI, machine learning, and operational use cases.
Develop and optimize ETL/ELT workflows using Apache Spark, Delta Lake, and medallion architecture.
Enable self-service analytics and GenAI use cases through governed data products, reusable APIs, semantic layers, and trusted data services.
Enable AI and data science workflows through feature-ready and model-ready datasets, curated context for GenAI applications, experimentation support, and repeatable delivery patterns.
Implement data quality, lineage, monitoring, validation, access controls, and privacy practices to ensure reliable and compliant data products.
Improve engineering processes, automation, delivery patterns, platform performance, scalability, and cost efficiency.
Partner with data scientists, analysts, software engineers, product teams, and business stakeholders to deliver measurable business outcomes.
Troubleshoot production issues, resolve root causes, and maintain reliable data platform operations.
Contribute to engineering standards, reusable frameworks, technical documentation, and a culture of data product thinking.
Influence technical direction, mentor engineers, document solutions, and promote strong engineering practices.