Data Engineer-Senior

Minimum 8 Years experience require:

Hands on Data Engineering (Key - Data modelling reviews + hands on

Data modeling reviewer design, implementation

Erwin used for data modelling

How to automate data modelling exercise using AI

Snowflake

Domain – SCM (1/2 modules) (Procumeremt, OM, logistics, inv or any other)

Intermediate experience in a relevant discipline area is required. Knowledge of the latest technologies and trends in data engineering are highly preferred and includes:

• Familiarity analyzing complex business systems, industry requirements, and/or data regulations
• Background in processing and managing large data sets
• Design and development for a Big Data platform using open source and third-party tools
• SPARK, Scala/Java, Map-Reduce, Hive, Hbase, and Kafka or equivalent college coursework
• SQL query language
• Clustered compute cloud-based implementation experience
• Experience developing applications requiring large file movement for a Cloud-based environment and other data extraction tools and methods from a variety of sources
• Experience in building analytical solutions
  Intermediate experiences in the following are preferred:
• Experience with IoT technology
• Experience in Agile software development

Core Responsibilities Unique to the Role

1. Lead the design, governance, and continuous evolution of enterprise data models and data products, including conceptual, logical, and physical data modelling. Review and approve data model changes, new source integrations, tables, attributes, and relationships introduced across data products to ensure alignment with enterprise data architecture standards, modelling principles, scalability, reusability, and business requirements.

2. Design and build reliable, high-performance data engineering solutions including data ingestion, transformation, integration, and orchestration pipelines across cloud data platforms, ensuring data quality, security, governance, scalability, and operational excellence throughout the enterprise data lifecycle across Supply Chain, Quality, Finance, Product Lifecycle, and other Enterprise Products domains.

3. Partner with Data Engineers, Solution Engineers, Analysts, and Business Stakeholders to translate business requirements into governed, discoverable, and AI-ready data products by applying Data-as-a-Product principles, metadata standards, lineage, semantic modelling, and data governance practices that enable trusted enterprise data

Required Skills, Education, or Experience

1. Strong experience in enterprise data modelling and data engineering, supporting large-scale data warehouses, lakehouses, semantic models, and enterprise data products on modern cloud platforms.

2. Strong expertise in enterprise data modelling, including conceptual, logical, and physical data models, dimensional modelling (Star/Snowflake), Data Vault, normalized and canonical models, semantic modelling, master data management, and enterprise data architecture standards.

3. Proven experience governing and reviewing enterprise data models across multiple data products, including evaluation of new source systems, entities, attributes, relationships, and model enhancements to ensure alignment with enterprise architecture principles, data standards, scalability, reusability, and long-term maintainability.

4. Experience designing and implementing data solutions across the complete data lifecycle, including Source Systems, Data Ingestion, Raw/Bronze, Curated/Silver, Business/Gold, Semantic Layers, and Enterprise Data Products, ensuring data quality, governance, security, performance, and operational excellence.

5. Strong hands-on expertise in data engineering and cloud data platforms, including Snowflake, Databricks, SQL, Matillion, dbt,Power BI with experience building scalable, high-performance data pipelines, data integration solutions, and reusable enterprise data assets.

6. Proven ability to lead technical design and data model reviews and collaborate with Solution Engineers, Data Engineers, Architects, and Business Stakeholders to translate business requirements into scalable data products and establish enterprise-wide data modelling, engineering, and governance best practices.

Preferred (Nice to Have) Skills, Education, or Experience

1. Experience implementing data quality frameworks for Supply Chain, Quality, Finance, Product Lifecycle, and other Enterprise Products domains.within large-scale Data Modernization or Data-as-a-Product initiatives.

2. Experience applying AI/ML and Data Observability capabilities to Data Quality, including anomaly detection, predictive quality monitoring, automated root cause analysis, AI-ready data certification, and governance controls supporting Analytics, Machine Learning, and GenAI solutions.