Data Engineer

PACCAR•Denton, TX

5d•Onsite

About The Position

This position is for a Data Engineer with the Peterbilt Advanced Analytics team. This team focuses on solving Peterbilt’s most important challenges using Data and Advanced Analytics. The Advanced Analytics team works on a huge variety of projects; from predictive analytics models to support Operations, to prescriptive analytics to support Peterbilt sales efforts, and data mining projects to identify the drivers of warranty claims. This team works on high-impact and high-visibility projects. Data Engineers present their work to senior executives, helping shape not only Peterbilt’s current business, but its long term strategy. The team embraces a collaborative approach to Data Science, sharing best practices and new ideas. Come join this dynamic, growing and pioneering team!

Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field. Master's degree preferred.
5+ years of professional experience in a data engineering or similar role.
1+ years of hands-on experience with AWS and related services (e.g., EC2, ECS, S3, SNS, Lambda, IAM).
Experience building and maintaining data pipelines using tools such as Informatica, Attunity, Snowflake, or comparable data integration and cloud data platforms.
Working knowledge of dimensional modeling (e.g., Kimball), ETL/ELT patterns, and data warehousing concepts (e.g., Snowflake, Redshift).
Experience with relational databases such as SQL Server and PostgreSQL; familiarity with NoSQL databases (e.g., MongoDB, DynamoDB) a plus.
Proficiency in Python and SQL; familiarity with Java, Scala, or C# a plus.
Exposure to big data technologies such as Spark.
Experience with BI tools such as Tableau to develop reports and dashboards that generate insights from data.
Experience contributing to projects in a collaborative, cross-functional team environment.
Strong analytical skills, attention to detail, and a solution-oriented approach.
Ability to communicate technical concepts clearly to both technical and non-technical audiences.
Self-motivated learner with a curiosity for new tools, techniques, and business problems.

Nice To Haves

Exposure to modern data stack tools such as dbt (data build tool) for transformation and Airflow for workflow orchestration.
Exposure to streaming data technologies such as Kafka or Kinesis.
Familiarity with data quality and observability tools such as Great Expectations, Soda, or Monte Carlo.
Familiarity with infrastructure as code (IaC) tools such as Terraform or CloudFormation.
Familiarity with CI/CD practices and tools (e.g., Jenkins, AWS CodePipeline).
Experience with containerized deployments using Docker, Kubernetes, or ECS.
Experience with shell scripting.
Exposure to or interest in supporting machine learning, generative AI, or LLM applications in production environments, including familiarity with vector databases or embeddings pipelines.
Experience operationalizing analytics APIs using frameworks such as Flask, Plumber, or Swagger.
Prior experience or interest in manufacturing, automotive, IoT, or telematics data domains.

Responsibilities

Build, maintain, and support automated data pipelines across a wide range of data sources using an AWS, Informatica, Attunity, and Snowflake technology stack, with a strong emphasis on scalability and automation.
Contribute to the overall data architecture, frameworks, and design patterns used to store, process, and manage large volumes of data.
Develop and implement solutions to measure, monitor, and improve data quality in alignment with business requirements.
Ensure product and technical features are delivered on time and in accordance with functional and technical specifications.
Design and implement reporting and analytics capabilities in collaboration with product owners, analysts, and business partners, using Agile/Scrum methodologies and tools such as Tableau.
Promote product health by developing automated, scalable, and sustainable solutions, with a strong focus on minimizing defects and reducing technical debt.
Provide post-implementation and production support for data pipelines and related data solutions.
Partner with business units to understand data engineering and IoT needs, define use cases, establish processes, and translate requirements into scalable solution designs.

Benefits

401k with up to a 5% company match
Fully funded pension plan that provides monthly benefits after retirement
Comprehensive paid time off – minimum of 10 paid vacation days (additional days are provided with additional seniority/years of service), 12 paid holidays, and sick time
Tuition reimbursement for continued education
Medical, dental, and vision plans for you and your family
Flexible spending accounts (FSA) and health savings account (HSA)
Paid short- and long-term disability programs
Life and accidental death and dismemberment insurance
EAP services including wellness plans, estate planning, financial counseling and more
This position is also eligible for a holiday gift.