Data Engineer Sr

At Oshkosh we design and manufacture some of the toughest specialty trucks and access equipment, serving soldiers, firefighters, and communities worldwide. As a key member of the Advanced Analytics team, the Data Engineer will develop and execute the data analytics strategy, applying it to Oshkosh products and services.

Responsibilities

• Design and develop scalable Change Data Capture (CDC) and ETL solutions to deliver data from source systems to analytics platforms (structured and unstructured; batch and streaming).
• Utilize tools such as Terraform, Spark, Databricks, Snowflake, Git, and Azure infrastructure services (ADLS Gen2, Azure Compute, Azure Data Factory, AI Foundry, AKS, VPCs, Storage Accounts, etc.).
• Assist in ensuring proper data governance—including quality and security—within the data lake and enterprise data warehouse across all business segments.
• Performance‑tune data processes and troubleshoot data‑processing issues.
• Collaborate, coordinate, and communicate across disciplines, departments, and segments.
• Develop rapid‑prototyping and design processes for fast solution delivery to the business.
• Maintain reference architecture and documentation for architectural governance and roadmap planning.
• Educate others on best practices for data modeling, database design, ETL design, job scheduling, and monitoring.
• Assist or lead feasibility studies and project estimates (manpower, budget, timelines) for research and development initiatives.
• Follow supervisory direction efficiently, provide clear technical feedback, and prioritize work based on business needs.
• Complete tasks on a mutually agreed schedule with minimal mentoring.

Requirements

• Bachelor’s degree in computer science, data science, or a related field.
• Four or more years of experience as a data engineer, ETL developer, and/or data‑warehouse DBA.
• Three‑to‑five years of experience in a Data Engineering or Analytics organization.
• Two or more years of software development experience (Python/Go preferred).
• Three or more years of experience with a major cloud provider (AWS, Azure, or GCP).
• Strong proficiency with SQL, Bash, and Python (including the Anaconda distribution).

Qualifications

• Two or more years operating in Unix/Linux and Windows server environments.
• Two or more years working in an Agile/Scrum environment.
• Experience with cloud‑based data services (Azure Synapse/Data Lake, AWS RedShift, Snowflake, GCP BigQuery).
• Experience partnering with Analytics and Data Science teams to build production‑grade GenAI/ML solutions.
• Familiarity with CDC solutions (Qlik Replicate, Striim, Debezium, etc.).
• Experience with big‑data tools and architectures (Cloudera Hadoop, HDFS, Hive, Spark).
• Working knowledge of telematics interfaces and streaming solutions (MQTT, NiFi, Kafka, etc.).
• Automotive or heavy‑duty vehicle data‑bus experience (Ethernet, CAN).
• Highly organized, detail‑oriented, with strong critical‑thinking, analytical, and problem‑solving skills.
• Ability to handle multiple tasks in a fast‑paced environment, both independently and as part of a team.
• Excellent interpersonal skills and the ability to present information clearly to leadership and peers.

Skills

• Terraform, Spark, Databricks, Snowflake, Git.
• Azure services: ADLS Gen2, Azure Compute, Azure Data Factory, AI Foundry, AKS, VPCs, Storage Accounts.
• SQL, Bash, Python (Anaconda).
• Data governance, quality, and security practices.
• Performance tuning and troubleshooting of data pipelines.
• Rapid prototyping and solution design.
• Reference architecture documentation and governance.

Benefits

• Professional development and a variety of skills‑training opportunities.
• Recognition as one of the World’s Most Ethical Companies™ for six consecutive years.
• Core values that emphasize putting people first, doing the right thing, perseverance, and teamwork.
• People‑first culture with a competitive total‑rewards package.

Pay

Pay Range: $104,100.00 – $173,900.00. The range reflects minimum and maximum target pay for the position across all U.S. locations; individual pay is determined by factors such as role scope, experience, education, skills, and internal equity.