Data Engineer

Role Summary

The Data Engineer designs builds andoperatesthe ingestion transformation and Lakehouse solutions that power Mascos enterprise POS and adjacent commercial data. This role delivers high-quality reliable and secure data pipelines against the standards and specifications set by the Enterprise Data Architect and the direction of the Data Engineering Leader. The engineer contributesacrossthe full pipeline lifecycle ingestion data quality transformation and enrichment including the pipeline-side execution of the enterprise attribution crosswalk. Depending on assignment the Data Engineer may focus more heavily on ingestionor onquality and pipeline development but the role covers all aspects.

What Youll Own

Ingestion & Pipeline Development

• Design build andmaintainautomated ingestion pipelines and multi-source integration forretailer HQ and BU data on the Databricks and Azure data stack.

• Develop RESTful APIs and API-based integrations forretailerand third-party dataacquisition.

• Delivercritical-pathpipelines and validation for priority data feeds.

• Integrate with orchestration tools and cloud or hybrid storage systems to enable end-to-end data workflows.

• Contribute to Lakehouse solutions using ACID-compliant storage layers schema enforcement and versioning for reliable data management.

Data Quality Validation & Monitoring

• Run data-quality checks at the pipeline level missing values outliers consistency and cross-source reconciliation.

• Implement monitoring validation and automated notifications within the data lifecycle to protect performance quality and availability.

• Support pipeline monitoring incident response and continuous improvement in partnership with the Data Engineering Leader.

• Maintain change control and testing processes for modifications to pipelines and data models.

Attribution & Master Data Support

• Build the ingestion side of the attribution crosswalk and master data foundations against the Enterprise Data Architects documented spec.

• Support maintenance of hierarchy mappings and attribute relationships through pipeline work.

• Surface data-quality issues mapping exceptions and structural gaps back to the Data Engineering Leader and Architect.

DevOps Automation & Data Preparation

• Implement CI/CD pipelines for data engineering workflows and automate testing deployment and monitoring of data solutions.

• Developmaintain and secure tables relationships metrics and calculations for analytical models supporting reporting and advanced analytics.

• Contribute to research and recommendations on data management products services and standards.

Documentation & Knowledge Management

• Document ingestion patterns pipelines validation rules and metric definitions as part of thedefinition ofdone.

• Contribute pipeline lineage and technical metadata to the enterprise catalog.

• Follow the documentation standardsestablishedby the Data Engineering Leader and Enterprise Data Architect.

Required Qualifications

Education & Experience

• Bachelors orMasters degree in Computer Science Engineering Data Analytics or a related field; equivalent professional experience considered.

• Substantial hands-on experience designing and building data engineering solutions on a major cloud platform with emphasis on the Azure ecosystem.

• Experience delivering production pipelines and supporting them in an operational setting.

Technical Skills

• Advanced hands-onexpertisewithDatabricksand theAzure data stack(Data Factory Data Lake Synapse Analysis Services).

• Strong working knowledge of themedallion architecture(bronze / silver / gold) for structuring Lakehouse solutions.

• Familiarity withMicrosoft Fabricand 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.

• Strongproficiencyin Python (or Scala) for data engineering.

• Experience with RESTful API development for data acquisition and ingestion fromretailerand 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 ofdata 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 technicaldetailclearly 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) andDataOps/MLOpspractices.

• Experience with Microsoft Tabular models and the DAX language.

• Experience with at least one data analysis tool such as Power BI Tableauetc.