Data Engineer and Modernization Leader

Role Overview 
The Data Engineering & Modernization Leader will drive the strategy, architecture, and delivery of next-generation data platforms across cloud and hybrid ecosystems. This role requires a seasoned leader with deep technical expertise, strong architectural vision, and the ability to guide teams through large-scale modernization initiatives. The ideal candidate brings hands-on experience with BigQuery, Spark SQL, Microsoft Fabric, and enterprise-level Big Data systems, along with the leadership ability to influence technology direction, mentor teams, and collaborate with cross-functional stakeholders. 

This is a high-impact role for someone who can balance strategic thinking with technical execution, ensuring data readiness, reliability, scalability, and modernization of legacy systems into modern cloud-native architectures. 

Key Responsibilities 

Leadership & Strategy 
Define and execute the enterprise data engineering and modernization roadmap across cloud, on-prem, and hybrid ecosystems.  Lead a team of data engineers, architects, and analysts in designing scalable, secure, and high-performance data platforms.  Partner with business, product, analytics, and data science teams to align data architecture with organizational goals.  Drive the adoption of modern data engineering best practices, including ELT patterns, CI/CD automation, and data governance frameworks.  Champion cloud transformation initiatives and guide teams through migration from legacy systems to GCP and Microsoft Fabric. 

Data Platform Modernization 
Architect and modernize large-scale data ingestion, transformation, and modeling pipelines.  Lead the migration of on-prem ETL workflows to Microsoft Fabric, optimizing for scalability, cost efficiency, and performance.  Oversee design of enterprise-grade data models (Dimensional, Data Vault, Lakehouse) enabling analytics and ML use cases.  Drive the implementation of unified metadata management, data quality monitoring, and stewardship processes. 

Technical Execution 
Provide deep technical expertise in BigQuery, Spark SQL, Fabric Notebooks, Python, and modern cloud data architectures.  Ensure optimal design and performance of BigQuery workloads through partitioning, clustering, and cost optimization.  Build and govern pipelines and dataflows in Microsoft Fabric (Gen2), integrating structured and semi-structured sources.  Enable real-time analytics by designing streaming solutions using Pub/Sub and similar cloud services.  Establish robust security models including data masking, row-level security, and role-based access controls. 

Collaboration & Stakeholder Engagement 
Work closely with analytics and BI teams to ensure seamless delivery of model-ready datasets for Power BI and other tools.  Collaborate with DevOps teams to enhance CI/CD pipelines, automated deployments, and testing frameworks.  Serve as a key advisor to leadership on data platform strategy, modernization opportunities, and technology investments. 

Mandatory Skills & Experience 
15+ years of experience in data engineering, with at least 5 years in a leadership role.  Strong expertise in Microsoft Fabric, BigQuery, Spark SQL, Python, and modern Big Data ecosystems.  Proven experience leading complex cloud transformation or data modernization initiatives.  Hands-on knowledge of data modeling techniques (Kimball, Data Vault), ETL/ELT, CI/CD, and governance.  Experience with Azure DevOps, Git, Power BI, GCP Console, Dataflow, and Dataproc.  Ability to manage and mentor engineering teams while driving enterprise-scale technical decisions. 

Preferred Qualifications 
Experience working with distributed teams in fast-paced environments.  Strong understanding of data security, compliance, and enterprise governance frameworks.  Exposure to ML feature engineering, real-time analytics, and metadata cataloging.  Excellent communication, stakeholder management, and leadership skills. 

Education 
Bachelor’s degree in Computer Science, Engineering, or a related field. A master's degree is an added advantage.