Overview
The Data Engineer designs, builds, and operates data ingestion, transformation, and Lakehouse solutions for enterprise POS and related commercial data.
What you'll do
- Design, build, and maintain automated ingestion pipelines and multi-source integration for retailer, HQ, and BU data on Databricks and Azure.
- Develop RESTful APIs and API-based integrations for retailer and third-party data acquisition.
- Deliver critical-path pipelines with validation for priority data feeds.
- Integrate with orchestration tools and cloud or hybrid storage to run end-to-end data workflows.
- Run data-quality checks (missing values, outliers, consistency, cross-source reconciliation) and implement monitoring/validation/notifications.
- Build ingestion-side attribution crosswalk and master data foundations per the Enterprise Data Architect’s spec.
- Implement CI/CD pipelines for data engineering workflows, including automated testing, deployment, and monitoring.
What you'll need
- Have a Bachelor’s or Master’s degree in Computer Science, Engineering, Data Analytics, or a related field (equivalent experience considered).
- Have substantial hands-on experience designing and building data engineering solutions on major cloud platforms with emphasis on Azure.
- Have experience delivering production pipelines in an operational setting.
- Have advanced hands-on expertise with Databricks and the Azure data stack (Data Factory, Data Lake, Synapse, Analysis Services).
- Be strong in Advanced SQL/T-SQL (queries, stored procedures/functions, indexes, partitions, DDL/DML) plus ETL/ELT design and build.
- Be proficient in Python (or Scala) for data engineering and in RESTful API development for ingestion.
- Know distributed processing with Apache Spark and Lakehouse principles (ACID storage, schema enforcement, versioning), plus CI/CD/DevOps/automation.
Nice to have
- Experience in retail, consumer goods, or manufacturing analytics where POS 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 or Tableau.
Details
- Location: Masco Home Products India.
- Employment type: Full time.
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