MS Fabric Senior Architect

Job Summary

Senior Architect position in a multinational organization delivering scalable data and analytics solutions built on Python LakeHouse platforms, including Spark Job Definition, OneLake SQL, and PySpark. This role demands deep expertise in retail banking data and processes, focused on secure, high-performing hybrid cloud data architecture that delivers business value. The work will be performed in a hybrid work model with day shift.

Responsibilities

• Architect durable LakeHouse-based data architectures that incorporate OneLake, Spark Job Definition, and PySpark components to enable large-scale retail banking analytics and reporting use cases
• Build end-to-end data pipelines using Python, SQL, and PySpark to ingest, transform, and curate transactional and customer data from multiple retail banking source systems
• Tune Spark Job Definition configurations and job orchestration to deliver reliable, predictable performance, cost efficiency, and high dependability for mission-critical banking workloads
• Develop secure data models and access patterns across LakeHouse and OneLake environments in accordance with retail banking regulatory requirements and internal risk policies
• Partner with product owners and retail banking domain experts to convert business objectives into technical data architecture blueprints and implementation roadmaps
• Lead engineering teams with best practices for coding standards, performance tuning, and reusable patterns using Python, SQL, and PySpark across hybrid environments
• Evaluate solution designs, technical specifications, and implementation plans to ensure they align with enterprise architecture standards and the long-term data strategy
• Work with cloud infrastructure and security teams to ensure LakeHouse and OneLake solutions meet expectations for resilience, observability, and compliance
• Produce detailed documentation covering data models, data flows, pipeline configurations, and operational runbooks to support sustainable day-to-day operations for delivery teams
• Mentor less experienced team members through constructive feedback, technical coaching, and examples of effective patterns for data engineering in banking contexts
• Engage with stakeholders to assess new data platform features and tools, determine their fit for retail banking use cases, and recommend adoption approaches
• Drive continuous improvement by analyzing production incidents, identifying root causes, and applying architectural enhancements that reduce risk and improve stability
• Ensure data solution outcomes support organizational goals by demonstrating how improved data quality, timeliness, and accessibility enhance customer experiences in retail banking

Qualifications

• Require extensive experience designing and implementing LakeHouse based data platforms with OneLake or comparable technologies in complex enterprise environments
• Require strong hands on expertise in Python SQL and PySpark for building scalable data pipelines, reusable libraries, and automation frameworks that support analytics and reporting
• Require deep domain knowledge of retail banking, including deposits, loans, cards, customer journeys, and regulatory reporting, to ensure solutions meet functional expectations
• Require proven experience working with Spark Job Definition, including job configuration, resource tuning, monitoring, and optimization for both batch and near real time workloads
• Require experience working in hybrid work environments, collaborating across global teams using structured documentation, version control, and standard agile practices
• Nice to have experience designing solutions that integrate LakeHouse platforms with downstream analytics tools, dashboards, and machine learning workflows in banking scenarios
• Nice to have exposure to data governance practices such as data cataloging, lineage tracking, and quality frameworks that enhance trust in banking data products
• Nice to have familiarity with performance engineering techniques on large relational datasets using advanced SQL optimization and partitioning strategies

Certifications Required

Preferred certifications include Microsoft Azure Data Engineer Associate or Databricks Data Engineer Professional or equivalent data engineering certifications