Ajay Kharade

About Ajay Kharade

Lead Data Engineer with 16 years of hands-on experience designing enterprise-scale data solutions that align technology with business objectives.• Leadership & strategy: Managed and mentored medium to large engineering teams, established enterprise data platforms and governance frameworks, and contributed Business Requirement Documents (BRD) and Technical Design Documents (TDD) in collaboration with business stakeholders.• Pre-sales experience: Partnered with sales and solution teams on RFP/RFI responses, crafted scalable architecture proposals, built POCs and demos, and presented solution roadmaps to senior stakeholders.• Modernized infrastructure: Led end-to-end cloud migrations from on-premises platforms to Microsoft Azure, Microsoft Fabric, AWS, and GCP, achieving up to 30% cost savings.• Driving innovation: Integral part of data practice innovation team. Developed GenAI-powered tools to analyze legacy SAS code, delivering significant storage savings; and designed multiple reusable data frameworks for the data ingestion, data quality & data processing. • BI Solutions: Built and optimized BI solutions for regulatory reporting and retail pricing/margin projects, improving load time and responsiveness using Power BI, Tableau, and Looker.• Optimized data pipelines: Built and maintained high-volume ETL systems using Spark, Kafka, and Delta Lake, enabling real-time analytics for Fortune 500 clients in the finance and retail sectors.• Domain expertise: Banking & Financial Services, Retail & Supply Chain, Healthcare and Technology.

Experience

1. Data Architect Lead Data Engineer

   Synechron

   May 2023 — Present · US

   Led migration of regulatory reporting systems (STARE – GL 14A, CCAR, CECL ACL, Fed) from on-premise databases and flat files to Azure Microsoft Fabric Lakehouse, defining source-to-target mappings, canonical finance schemas, medallion layers, Delta table structures, and reconciliation controls.• Designed and published Power BI semantic models for regulatory reporting, enabling standardized, controlled consumption by finance, risk, and regulatory teams.• Developed data ingestion, transformation, and remediation pipelines using Hadoop (HDFS, Hive), Spark/PySpark, and SQL, incorporating CDC/incremental logic, validation rules, balancing checks, exception handling, and auditability.• Implemented governed data access and BI integration, including row-level/object-level security, entitlement mapping, dataset certification, and automated refresh orchestration.• Modeled finance and risk datasets to FIB-DM / FSDM standards, mapping legal entities, chart of accounts, hierarchies, KPIs, risk attributes, and regulatory dimensions, embedding calculations and validation logic directly into curated datasets.• Authored low-level design (LLD) and deployment artifacts, including physical data models, pipeline orchestration diagrams, environment promotion strategies, and cutover runbooks.• Supported Tier-1 financial clients via RFP responses, technical walkthroughs, and client demos, demonstrating enterprise-grade regulatory reporting solutions.• Built and validated proof-of-concept solutions, including:o AWS DataZone Data Mesh, defining domain-aligned data products, ownership boundaries, metadata publishing workflows, and cross-domain access patterns.o A GenAI-based legacy code migration accelerator and an ontology-driven semantic context framework to enhance AI agent accuracy in regulatory and finance domain use cases.

Education

• Annasaheb Dange College of Engineering and Technology, ASHTA

  Bachelor of Engineering (B.E.), Computer Science