Data Engineer - (Only W2)

Data Engineer 6 12-month contract + Hybrid New Brighton MN; potential for remote
The Opportunity: This opportunity is supporting the client as it establishes and implements the strategy around business platforms, including but not limited to the Field Service Management (FSM), Enterprise Resource Planning (ERP), Human Capital Management (HCM), and Customer Relationship Management (CRM) tools. The intent is to transition operating companies and branches onto platforms in a strategic manner that optimizes and expedites value capture as well as minimizes disruption to the business. This program will also encompass legal entity rationalization and shared services establishment discussions as necessary to facilitate the system and platform moves. The scope of the program includes both business transformation and technology across the company in several phases over the next 2-3 years.
Position Summary:
This data engineer will specifically provide support for the ExCAL program as well as key stakeholders across the organization, including being responsible for design, implementation, automation, and maintenance of large-scale enterprise ETL processes for a global client. This individual will serve as an expert data resource within the team handling data delivery to deliver on-time, accurate, high-value, robust data solutions across multiple platforms. This position will work closely with the platform team(s) to engage stakeholders across operating companies, corporate functions, and vendor partners to successfully deliver project objectives and outcomes within the selected systems.
Summary of Responsibilities:
Critical Responsibilities include curating the data to match what the reporting analysts need and partnering to build the pipeline and BI reports for data analysts.

• Design, create, and maintain specific data models and schemas that align with ExCAL application requirements (Oracle, GEP, UKG, Passport, MS CRM) to ensuring data integrity, consistency, and accuracy across functional modules (e.g., Finance, Supply Chain, Manufacturing, HR).
• Support the end to end data architecture that enabling seamless data flows between legacy systems, the new platforms, and downstream applications.
• Support development, testing, and maintenance of data pipelines and ETL processes that facilitate complex data conversions, integrations, and ongoing data synchronization during the multi-phase implementation.
• Adhere to the governance and requirements of enterprise data warehouses and integration layers used to support reporting, analytics, and cross-functional business processes.
• Develop scalable and repeatable processes to support the ingestion, cleansing, transformation, and validation of large volumes of data
• Create transformation logic to convert raw legacy data into structured, target system ready datasets meeting functional and technical specifications.
• Ensure secure, efficient, and reliable data migrations across all platforms, including APIs, middleware platforms, and integration hubs.
• Ensure compliance with enterprise data governance, privacy, and security standards throughout all phases of the implementation lifecycle.
• Build strong, trust-based relationships with cross-functional teams, including business SMEs, technical architects, implementation partners, and global delivery teams.
• Contribute to optimization of program processes, ensuring streamlined, scalable, and repeatable approaches for data migration, data quality management, and system integrations.
• Apply industry best practices such as version control, peer code reviews, data validation frameworks, automated testing, and deployment pipelines within program workstreams.
• Support the team s automation capabilities by developing repeatable tools, templates, and scripts to support ongoing migration cycles, integration monitoring, and data quality assurance.

Skill Set Requirements:

• 3-5 years of relevant work experience with big data technologies, data engineering, data modeling, ETL processes, and database management for on-prem and cloud-based data environments.
• Proficient within Data Processing/Streaming tools such as Databricks, Snowflake, Kafka
• Strong programming skills in languages like Python, Java, or Scala.
• In-depth knowledge of SQL and NoSQL databases.
• Data governance, data quality, and data security practices.
• Advanced Excel capabilities.
• Training/Experience with data science, machine learning, or other advanced analytical techniques as well as database management technologies (in data analytics context) is preferred.
• Experience working in a hybrid delivery model that spans time zones.

Strong attention to detail and organization skills.