Senior Software Engineer (Data Engineering and Infrastructure)

Aurora•Pittsburgh, PA

2d•Hybrid

About The Position

Aurora is seeking a Senior Software Engineer to join its data engineering and infrastructure team. The role contributes to the design, development, and maintenance of a scalable, reliable data platform, including core data lake and data warehouse infrastructure, data pipelines, and tools that process data at massive scale. The systems should make data accessible, high-quality, and secure to support organizational insights and product innovation. The engineer will build and maintain data pipelines and ETL/ELT processes, develop cloud-based data infrastructure, and collaborate with data scientists, analysts, autonomy engineering, and product teams. The role also focuses on improving system performance, reliability, and cost-efficiency; operational monitoring; and data governance, quality, and security. The engineer will work with senior team members and management to improve data ecosystem tooling, user experience, and the team roadmap. Aurora expects employees to work from the office at least three days per week. Some roles may require more frequent in-office presence, up to five days per week, based on operational needs. The position is based in Pittsburgh, Pennsylvania, or Mountain View, California. The stated base salary ranges are $146,000–$234,000 per year in Pittsburgh and $162,000–$260,000 per year in Mountain View. The successful candidate is also eligible for an annual bonus, equity compensation, and benefits.

Requirements

Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
At least 5 years of professional software engineering experience, focused on data-related projects.
Proficiency in at least one programming language commonly used for data engineering, such as Python, Go, or C++.
Solid experience with big data processing frameworks such as Presto/Trino, EMR, Apache Spark, Flink, Kinesis Data Stream, or similar technologies.
Hands-on experience with cloud platforms such as AWS, GCP, or Azure and their data services, such as S3, Redshift, BigQuery, or Glue.
Strong knowledge of SQL and experience with relational and NoSQL databases.
Intermediate knowledge of data analytics infrastructure, including data transformation tools such as DBT and visualization frameworks and tools.
Experience building and managing data pipelines using an orchestrator such as Apache Airflow.
Ability to systematically approach open-ended questions and identify pragmatic data solutions that scale.
Ability to work effectively in a highly cross-functional, fast-moving, and high-stakes environment.
Proven ability to communicate technical, data-driven solutions to technical and non-technical audiences across stakeholders.

Nice To Haves

Experience with AI tooling, LLMs, and agentic frameworks.
Experience with data warehousing solutions such as Snowflake or data lake architectures.
Familiarity with modern data stack tools and practices.
Passion for building elegant, scalable, and maintainable systems.
Experience using Amazon Web Services (AWS) tools.

Responsibilities

Design, build, and maintain scalable data pipelines and ETL/ELT processes to ingest, transform, and load data from various sources into the data warehouse.
Develop and manage data infrastructure components using AWS cloud services and infrastructure-as-code tools such as Terraform.
Collaborate with data scientists, analysts, autonomy engineering teams, and product teams to understand data needs and develop appropriate solutions.
Optimize data processing systems for performance, reliability, and cost-efficiency.
Implement monitoring, alerting, and logging for data pipelines and infrastructure to support operational stability.
Promote best practices in data governance, data quality, and security.
Work with senior team members and management to improve data ecosystem tooling, refine user experience, and continuously improve the team roadmap.