Data Engineer

W S DARLEY & CO•Itasca, IL

7h•$75,000 - $95,000•Onsite

About The Position

W.S. Darley & Co. is seeking a Data Engineer to build systems and infrastructure that collect and process business data in support of company needs and goals. The role turns business data into a trusted foundation for decision-making, automation, and AI use. It combines practical data engineering with analytics enablement, including developing statistical models for classification and information extraction; maintaining and using existing data infrastructure to fulfill data requests; and performing data housekeeping, cleansing, normalization, hashing, and data model changes. The Data Engineer analyzes data to identify anomalies, trends, and correlations, and troubleshoots issues to find solutions for end users. The role works cross-functionally with infrastructure, reporting, security, and AI colleagues, business stakeholders, and external specialists as needed. The position is full-time and is expected to work from Darley’s Itasca, Illinois office four to five days per week. The regular schedule is Monday through Friday, 8:00 a.m. to 4:30 p.m., or as otherwise required to meet business needs. Occasional evening and weekend work may be required to support sales events, customer needs, and critical incidents. Regular travel is not required, though occasional travel to customer sites, OEMs, Darley sites, management meetings, or training events may be requested.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Data Science, Mathematics, Statistics, or a related field; an equivalent combination of education, technical training, and relevant experience may be considered.
Two to five years of experience in data engineering, data integration, business intelligence, analytics engineering, or a related field.
Experience building and maintaining production data pipelines.
Experience integrating data from ERP, CRM, manufacturing, financial, operational, and third-party systems.
Experience working with business stakeholders to translate requirements into scalable data solutions.
Strong SQL skills, including query optimization, data transformation, and database design.
Proficiency in Python for data processing, automation, and ETL/ELT development.
Experience with data warehousing concepts, dimensional modeling, and semantic data structures.
Knowledge of data quality management, data validation, cleansing and reconciliation, and monitoring techniques.
Experience with APIs, flat files, and structured or semi-structured data sets.
Understanding of data governance, security, and access management principles.
Strong analytical and problem-solving skills, including the ability to identify trends, anomalies, and data quality issues.
Ability to prioritize competing requests and deliver practical solutions.
Strong communication skills with technical and non-technical audiences.
Ability to document technical solutions and establish repeatable processes.
Demonstrated customer service mindset when supporting internal stakeholders.
Ability to work cross-functionally and influence decisions through data expertise.
Commitment to continuous learning in emerging data and AI technologies.

Nice To Haves

Advanced degree.
Experience supporting reporting, analytics, or AI initiatives.

Responsibilities

Build and maintain SQL- and Python-based data pipelines that ingest, validate, and transform data from business systems, APIs, files, and approved external sources.
Help implement warehouse structures and dimensional models, including reusable tables, documented relationships, and consistent business definitions.
Partner with infrastructure colleagues on secure connectivity, access, environments, scheduling, monitoring, and recovery requirements.
Implement data quality checks, reconciliation, error handling, version control, and documentation to keep data products reliable and maintainable.
Work with reporting colleagues to deliver curated data sets and semantic models for dashboards and self-service analytics.
Translate business questions into data requirements and prioritize useful, achievable solutions with stakeholders.
Collaborate with external specialists on architecture and implementation patterns, then maintain and extend the solutions internally.
Develop statistical models for classification and information extraction from data.
Perform data housekeeping, cleansing, normalization, hashing, and required data model changes.
Analyze data to identify anomalies, trends, and correlations; troubleshoot problems, identify possible solutions, and resolve issues for end users.