Data Engineer at MJ Holding Company LLC

Bridgeview, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

29 Jun, 26

Salary

0.0

Posted On

31 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Python, Data Architecture, Data Engineering, ETL, Data Modeling, Analytics, Business Intelligence, Data Quality, Governance, Cloud Platforms, Performance Tuning, Data Warehousing, BigQuery, Snowflake, Databricks

Industry

Consumer Services

Description

Description Join the Leader in Trading Cards! MJ Holding Company, LLC is largest distributor of sports trading cards and trading card games like Magic: The Gathering and Pokémon—and we’re growing fast. Our success comes from passionate people who love what they do. At MJH, you’ll find a team that’s enthusiastic about the products we distribute, proud of the culture we’ve built, and committed to our success. If you’re driven and excited about the world of trading cards, we want you on our team! The Data Engineer is a core technical leader within the data team, responsible for the design, architecture, and delivery of scalable, reliable data platforms that power the organization’s data warehouse, analytics, and business intelligence initiatives. This role goes beyond hands-on development to include data architecture, solution design, technical direction, and mentorship. The Data Engineer partners closely with forecasting, application development, and business stakeholders to ensure the data ecosystem supports current and future business needs. Requirements Essential Duties and Responsibilities Data Architecture & Design Design and evolve the organization’s data architecture, including ingestion patterns, transformation layers, semantic models, and consumption frameworks. Lead architectural decisions for data warehousing and analytics platforms such as Snowflake, Databricks, BigQuery, or similar technologies. Define and enforce standards for data modeling, naming conventions, schema design, and performance optimization. Evaluate and recommend tools, technologies, and architectural patterns that align with scalability, reliability, and cost efficiency goals. Data Engineering & Platform Development Design, build, and optimize robust ELT/ETL pipelines using tools such as dbt, Apache Airflow, and cloud-native services (e.g., Azure Data Factory). Own end-to-end pipeline reliability, including orchestration, monitoring, alerting, and failure recovery. Develop and optimize complex SQL transformations, stored procedures, and performance-critical queries. Implement scalable data processing solutions that support both batch and incremental/near-real-time use cases. Data Modeling, Analytics & BI Enablement Lead the design of enterprise data models, including dimensional (star/snowflake) and curated analytical models. Partner with analytics and business teams to translate requirements into semantic models, KPIs, and certified datasets. Ensure the data warehouse is optimized for self-service BI, ad hoc analysis, and dashboard performance in tools such as Power BI or Tableau. Provide technical guidance on BI performance tuning, DAX/MDX best practices, and data usage patterns. Data Quality, Governance & Security Define and implement data quality frameworks, including validation, testing, and anomaly detection using tools such as dbt tests, Great Expectations, and custom Python solutions. Support data governance initiatives by helping establish data ownership, documentation, lineage, and access controls. Ensure data solutions adhere to security, privacy, and compliance requirements. Leadership, Collaboration & Mentorship Serve as a senior technical resource and mentor junior data engineers and analysts. Lead technical design discussions, architectural reviews, and documentation efforts. Collaborate cross-functionally with business stakeholders, product teams, and engineering partners to deliver well-designed data solutions. Influence data strategy and roadmap planning by providing architectural insight and trade-off analysis. Qualifications Competencies Expert-level SQL skills and strong proficiency with Python or similar scripting languages. Strong understanding of modern data architectures, cloud data platforms, and distributed data processing. Ability to communicate complex technical concepts to both technical and non-technical stakeholders. Proven analytical, problem-solving, and system-thinking mindset. Experience working in collaborative, version-controlled environments using ITIL practices. Self-directed, proactive, and comfortable owning ambiguous or complex problem spaces. Education Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field. Experience 5 - 7+ years of experience in data engineering, analytics engineering, or a closely related role. Extensive hands-on experience designing and building data warehouses and analytics platforms in Snowflake, Azure SQL, Databricks, BigQuery, or similar technologies. Advanced experience with data modeling, including dimensional and analytical models. Strong background in performance tuning, query optimization, and large-scale data transformation. 3+ years of hands-on experience developing BI solutions (Power BI, Tableau, or similar), including semantic modeling, DAX/MDX, and performance optimization. Experience translating business requirements into scalable, well-architected data solutions. Demonstrated ability to lead or influence technical design decisions. Preferred Qualifications Experience working with large-scale data lakes or similarly complex, high-volume data environments, including the design, optimization, and governance of structured and semi-structured data. Familiarity with data governance, data catalogs, metadata management, and data quality frameworks. Prior experience in retail distribution, category management, or a similar industry is a plus. Experience mentoring engineers or acting as a technical lead on data initiatives.

Responsibilities

The Data Engineer is responsible for the design, architecture, and delivery of scalable data platforms that support the organization’s data warehouse and analytics initiatives. This role includes data architecture, solution design, technical direction, and mentorship.