Archived listing. The details below describe a past opening.
What Matters at Magid? YOU do!
"The number one key to growth is having good people and that’s what has driven us at every stage of the game." - Greg Cohen, CEO
At Magid, we’re not just passionate about safety - we’re passionate about people. As an industry leader, we’ve built an innovative and collaborative environment where diversity is celebrated, ideas are valued, and personal and professional growth never stops.
Job Summary
The Data Engineer plays a crucial, cross-functional role here at Magid. This is a high-visibility role where your efforts will have impact on all levels of the organization. Our work spans product sourcing, customer journeys, service delivery, sales workflows, and the platforms and SME’s that support them. We have seen a drastic increase in adoption of the Data Engineer’s services. We are embedding data culture into our DNA and are excited to add a new face to that mission.
Essential Responsibilities
- Data pipelines and transformations (ingest, clean, vet, test, transform, publish)
- Well-documented datasets and advanced semantic models that enable reporting and analysis
- Data quality checks (freshness, completeness, validity) and participation in monitoring/alerting
- Datasets that support machine learning use cases with clear definitions
- Incremental improvements to pipeline performance, cost, and reliability with guidance
- Collaboration with partners to clarify requirements and iterate on data products
- Partner in Data Discovery & Solution Shaping
- Develop Power BI Solutions that are iterative while supporting our current ecosystem of analytics driven reporting
- Learn source systems and data flows; help map entities, identifiers, and key business rules
- Contribute to data modeling and design decisions with guidance (schemas, grain, slowly changing dimensions, etc.)
- Propose simpler, more reliable approaches (e.g., reuse shared datasets, standardize definitions) to improve trust and increase adoption
Build & Maintain Data Pipelines
- Build and maintain batch and/or streaming pipelines to ingest data from source systems into our analytical platform
- Develop transformations to clean, standardize, and enrich data using agreed-upon patterns and tools (e.g., SQL, Python, Fabric Data Lake, KQL)
- Support ML workflows by helping produce curated training datasets and feature-ready tables, following established patterns
- Help monitor pipeline health and data quality; investigate variances and propose code enhancements to key datasets.
Contribute to a Strong Data Culture
- Help evolve data standards (naming conventions, modeling patterns, documentation) to improve consistency and reuse
- Promote a culture of data trust through quality checks, clear definitions, and thoughtful change management
- Willingness to tackle obscure requests and find ways to solve cumbersome outdated workflows
How We Work
- Empowered to solve problems, not just build features
- Accountable for outcomes, not output
- Collaborative by default, from discovery through delivery
- Continuously learning, using data, AI/ML and customer insight to improve
Hybrid Work Schedule: Monday - Thursday onsite at our corporate office in Romeoville, IL; Fridays WFH.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field
- Equivalent practical experience is equally valued
- Strong SQL fundamentals (joins, aggregation, window functions, performance basics)
- Data modeling mindset: Cares about clear definitions, grain, and making data usable
- Pragmatic problem solving: Debugs issues, makes sensible tradeoffs, and knows when to ask for help
- Ownership: Takes responsibility for assigned datasets/pipelines and follows through to production
- Collaboration: Works effectively with product managers to deliver trusted data
Key Qualifications
- Minimum of 5+ years of experience in data engineering, analytics engineering, or software engineering (including internships or equivalent projects)
- Ability to write production-quality SQL and create reliable transformations with attention to correctness
- Understanding of dimensional modeling and/or event modeling concepts (fact/dimension tables, star schemas)
- Proficiency in Python (or similar) and comfort using Git and code reviews to collaborate
- Familiarity with data platforms (data warehouse/lakehouse concepts), and exposure to orchestration/ETL tools (e.g., Airflow, dbt, Spark) is a plus
Preferred Qualifications
- Experience working with a modern data warehouse/lakehouse (e.g., Microsoft Fabric One Lake, Snowflake, BigQuery, Databricks)
- Exposure to data quality testing, monitoring, or observability concepts
- Familiarity with data governance concepts (Row-Level-Security, Workspace Roles, etc)
- Exposure to machine learning workflows (training data preparation, feature tables, model experimentation support)
- Familiarity with modern engineering practices (CI/CD, testing, observability)