Data Engineer

MHC Kenworth•Leawood, KS

7d•Onsite

About The Position

Murphy-Hoffman Company, LLC is North America’s largest Kenworth truck Dealership and Leasing group. As MHC continues to grow, we have an opening for a Data Engineer. The Data Engineer designs, builds, and supports scalable data products and platform capabilities that enable trusted, timely, and governed data across the organization. This role develops and maintains batch and real-time data pipelines, integrates data from operational and third-party systems, and transforms raw data into analytics-ready assets for reporting, self-service analytics, operational intelligence, and advanced analytics use cases. The Data Engineer partners closely with data architecture, analytics, and business teams to deliver robust solutions across the modern data stack. This position is fully in office 5 days a week in Leawood, KS.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Management, Analytics, Engineering, or a related field; equivalent practical experience may be considered.
Demonstrated experience designing and building data pipelines, data integration solutions, and analytical data models in cloud data platform environments, with a strong understanding of data warehousing, dimensional modeling, lakehouse concepts, and enterprise data best practices.
Strong proficiency in SQL and data transformation techniques, with hands-on experience using Python and/or PySpark for pipeline development and data processing, as well as modern engineering practices such as dbt, source control, testing, documentation, and CI/CD.
Experience with Snowflake and/or Microsoft Fabric for data engineering, storage, transformation, and analytics enablement.
Experience working Azure Data Factory (ADF) or comparable orchestration tooling for enterprise data movement, scheduling, and workflow automation.
Experience with Confluent Cloud and Apache Kafka concepts, including topics, partitions, schemas, event-driving processing, streaming integration patters, and CDC / near-real-time data delivery.
Ability to work in a team environment and independently and collaboratively in a fast-paced environment with multiple priorities and changing requirements, with strong written and verbal communication skills and the ability to explain technical issues, tradeoffs, and recommended resolutions to diverse audiences.

Responsibilities

Designs, develops, tests, deploys, and maintains scalable data pipelines, ingestion solutions, and data integration processes that support batch and real-time / streaming workloads using tools and services such as Azure Data Factory (ADF), Azure Kubernetes Service (AKS), Confluent Cloud, Kafka, and Flink.
Implements and supports change data capture (CDC), event-driven, and streaming architecture patterns, including Kafka topics, schema-aware ingestion, and near-real-time data delivery to downstream consumers.
Develops data transformations and curated datasets using SQL, T-SQL, dbt, Python, and PySpark to support reporting, analytics, data science, and AI / machine learning use cases.
Creates and maintains data models, lakehouse / warehouse structures, and semantic-ready datasets that enable scalable, trusted, and high-performing downstream analytics and reporting.
Collaborates with data architects, analysts, application teams, and business stakeholders to define source-to-target mappings, business rules, data contracts, and solution designs that align with enterprise standards and business needs.
Builds and maintains reusable frameworks, templates, engineering standards, and deployment practices; including CI/CD, version control, and release processes for notebooks, dbt models, SQL objects, and pipeline definitions to improve consistency, scalability, speed, and long-term maintainability across the data platform.
Applies data quality, testing, reconciliation, monitoring, and documentation practices to ensure data completeness, accuracy, freshness, reliability, supportability, knowledge transfer, and auditability.
Troubleshoots data platform issues, production incidents, and root causes; communicates impacts and recommended resolutions clearly to technical and business stakeholders; and stays current on modern data engineering patters, platform capabilities, and security / governance requirements to recommend improvements that increase platform maturity and business value.
Performs other duties as assigned by supervisor.