Data Engineer

Data Quality, Validation & Monitoring

Attribution & Master Data Support

DevOps, Automation & Data Preparation

Documentation & Knowledge Management

Education & Experience

Technical Skills

• Advanced hands-on expertise with Databricks and the Azure data stack (Data Factory, Data Lake, Synapse, Analysis Services).
• Strong working knowledge of the medallion architecture (bronze / silver / gold) for structuring Lakehouse solutions.
• Familiarity with Microsoft Fabric and how it fits alongside Databricks in a modern enterprise data platform.
• Advanced SQL / T-SQL (queries, stored procedures, functions, indexes, partitions, DDL, DML) and strong ETL/ELT design and build experience.
• Strong proficiency in Python (or Scala) for data engineering.
• Experience with RESTful API development for data acquisition and ingestion from retailer and third-party sources.
• Working knowledge of distributed processing (Apache Spark) and Lakehouse principles — ACID storage, schema enforcement, versioning.
• Solid understanding of CI/CD, DevOps, and automation for data workflows.
• Strong understanding of Kimball dimensional modeling, including snapshot/transactional fact tables and conforming dimensions.
• Working knowledge of data constructs including keys, constraints, data types, normalization, referential integrity, and data quality analysis.
• Understanding of data governance, security, and compliance as they apply to data engineering.

Skills & Competencies

• Detail-oriented and thorough. Considers all aspects of a data problem, including how the small pieces relate to the larger picture.
• Highly self-motivated and self-directed. Operates with minimal oversight on complex, hands-on data work.
• Delivery-focused . Owns commitments and follows through.
• Willingness to explore and understand new and modern data tools to add value to the enterprise POS and POS-related engineering space.
• Collaborative with the Data Engineering Leader, Enterprise Data Architect, and BI Developers on shared pipelines, models, and metric definitions.
• Continuous learner who stays current on modern data engineering practices and tools.
• Strong communicator who can explain technical detail clearly to both engineering and analytics peers.

Preferred Qualifications

• Experience in retail, consumer goods, or manufacturing analytics environments where POS, sell-in, inventory, and third-party retail data are core.
• Experience with Databricks Unity Catalog, data lineage, and observability tools.
• Familiarity with ML/AI integration ( MLflow , Azure ML) and DataOps / MLOps practices.
• Experience with Microsoft Tabular models and the DAX language.
• Experience with at least one data analysis tool such as Power BI, Tableau, etc.