Senior Data Engineer

1. Data Product Engineering & Delivery 

• Partner with business stakeholders to translate business requirements into technical designs and acceptance criteria. 

• Design, build, and maintain scalable and reusable data pipelines and transformations using Azure and Databricks. 

• Implement robust ingestion frameworks, standardized patterns, and repeatable workflows aligned to enterprise engineering practices. 

• Build and optimize physical and semantic data models supporting analytics, reporting, and AI/ML use cases. 
   

2. Technical & Architectural Implementation 

• Apply best practices related to performance tuning, cost optimization, security, and reliability. 

• Ensure alignment with enterprise architecture, including shared infrastructure, canonical models, and platform standards. 

• Implement efficient data processing patterns (e.g., Delta Lake, medallion architecture, orchestration frameworks). 
   

3. Governance, Quality, & Operations 

• Embed governance-by-design principles including lineage, metadata, documentation, and certification standards. 

• Implement data quality rules, monitoring, and automated checks to ensure accuracy, completeness, and trust. 
   

4. Collaboration Across Domains 

• Work closely with business domain teams to validate that data products meet end-user needs and match business definitions. 

• Partner with Platform Engineering on pipeline frameworks, infrastructure patterns, and optimization. 

• Collaborate with Data Operations & Enablement to ensure strong documentation, certification, and governance coverage. 

Minimum Knowledge, Skills, and Abilities 

 

• Data Engineering & Pipelines: Strong experience with ETL/ELT design, distributed processing, pipeline optimization, and enterprise scale data workflows.  

• Cloud & Platform Expertise: Hands-on experience with Databricks and cloud data platforms such as Azure (preferred) or AWS, including data lakes, orchestration, and scalable compute. 

• Programming & Development: Proficient in Python, SQL, modular coding, APIs, source control, and automation tools.  

• Data Modeling & Architecture: Skilled in dimensional and semantic modeling, database design, and building performant, governed analytics-ready structures.  

• Data Quality, Metadata & Curation: Capable of cleansing, transforming, and validating data; familiar with lineage, cataloging, and data quality frameworks.  

• Collaboration & Communication: Able to translate business needs into technical solutions and clearly communicate technical concepts in cross-functional settings. 

Experience and Education  
 

• Bachelor's/Master's in Data Science, Engineering, or a related field. 

• 5 – 7 years of experience in data, analytics, or engineering roles 

• Proficiency in one or more general-purpose programming languages (e.g., Java, C/C++, C#, Python, JavaScript). 

• Working knowledge of SQL-based and NoSQL technologies (e.g., PostgreSQL, MySQL, MongoDB)