Data Engineer

About the role

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. The Data Engineer plays a crucial, cross-functional role with high visibility, impacting product sourcing, customer journeys, service delivery, sales workflows, and supporting platforms and subject-matter experts.

Responsibilities

• Design, build, and maintain data pipelines and transformations (ingest, clean, vet, test, transform, publish)
• Create well-documented datasets and advanced semantic models that enable reporting and analysis
• Implement data quality checks (freshness, completeness, validity) and participate in monitoring/alerting
• Develop datasets that support machine learning use cases with clear definitions
• Make incremental improvements to pipeline performance, cost, and reliability with guidance
• Collaborate with partners to clarify requirements and iterate on data products
• Participate in Data Discovery & Solution Shaping
• Develop iterative Power BI solutions that support the 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 (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 the 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
• Monitor pipeline health and data quality; investigate variances and propose code enhancements to key datasets

Contribute to a Strong Data Culture

• 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
• Tackle obscure requests and find ways to solve cumbersome, outdated workflows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

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• 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)

The salary range for this position is $96,000.00 – $134,400.00. Actual pay will be adjusted based on job-related factors permitted by law, such as experience and training, geographic location, additional qualifications such as licensure or certifications, market factors, and responsibilities.