Data Architecture Manager Remote Data Entry Candidate (Note: concise

Madhav Ghimire

Technology – AI & Data Data Architecture Manager (Life Sciences)

+1-720-***-**** ********@*****.*** LinkedIn: www.linkedin.com/in/madhav-ghimire146

Professional Summary

Data Architecture Manager with 11+ years delivering enterprise data platforms across AWS, Azure, and Snowflake for Accenture client engagements. Leads the design, governance, and modernization of data architectures that unify, enrich, and surface insights for healthcare and life-sciences–adjacent programs. Deep experience in conceptual, logical, and physical modeling; integration architecture; and end-to-end delivery from analysis through testing, UAT, and production. Trusted partner to business and technology leaders—facilitating workshops, aligning data strategy to outcomes, and mentoring cross-functional teams (data engineers, BI, QA) to ship secure, scalable solutions.

Skills & Attributes for Success

Stakeholder Collaboration: Workshops with business leads, data scientists, BAs, and IT; translate use cases/personas into data strategies

Architecture: Blueprints on AWS/Snowflake/Databricks/Azure; platform modernization; cloud reference architectures

Modeling: Conceptual, logical, physical layers; ER & dimensional; schema-on-read; semantic layers supporting BI/ML

Integration: Data acquisition and basic transformations; ETL/ELT patterns; event-driven pipelines (Kafka/Kinesis)

Design Reviews: Usability, efficiency, naming conventions; source-to-target mapping and transformation logic

Leadership: Manage timelines/cost/quality; mentor engineers; code/design reviews; sprint ceremonies

Security & Governance: Data security in motion/at rest; lineage, sensitivity labels; RBAC/ABAC; compliance

Innovation: Big-data patterns (Lambda/Kappa); performance benchmarking; AI/metadata enrichment awareness

Core Technical Skills

AWS: Redshift, S3, Glue, Lambda, Kinesis, Athena; cost/performance tuning

Azure: Synapse, ADF, ADLS Gen2, Databricks; Microsoft Purview for governance

Snowflake: Warehouses, schemas, Streams/Tasks; medallion (Raw/Bronze/Silver/Gold); SQL optimization

Databricks: PySpark/Spark SQL; orchestration; CDC and batch pipelines

BI & Analytics: Power BI semantic models, Direct Lake, RLS; curated datasets

DevOps: Azure DevOps/GitHub - CI/CD for SQL/DDL, notebooks, pipelines

Modeling Tools: ER Studio, Erwin; data dictionaries; lineage diagrams

Professional Experience

Data Architecture Manager / Data Architect Accenture – Client Engagements (Healthcare & Life Sciences Company) Dec 2022 – Present

Organized and led stakeholder workshops to capture requirements, personas, and key processes; produced domain roadmaps and architecture blueprints.

Led source-system analysis (structures, business rules, data quality constraints) and authored source-to-target mappings and transformation logic.

Designed end-to-end architectures on AWS, Snowflake, and Azure—ingestion transformation consumption—aligned to enterprise standards.

Owned conceptual, logical, and physical model development; enforced naming conventions, semantic consistency, and documentation in central repository.

Defined extraction and refresh strategies; guided engineers on pipelines (ADF/Glue/Databricks) and performance tuning.

Conducted design/code reviews across SQL, pipelines, and notebooks to ensure quality and alignment with architecture.

Supported QA/UAT with test strategies, validation rules, and reconciliation; led go-live readiness and post-deployment monitoring.

Championed security and governance (lineage, sensitivity labels, RBAC/ABAC) and performance benchmarking for enterprise applications.

Presented architecture proposals and solution options to senior client technology leaders; influenced adoption and delivery sequencing.

Data Architect / Data Engineering Lead Accenture – Client Engagements May 2020 – Dec 2022

Modernized data platforms on Azure and AWS; aligned domain models to semantic layers for BI and ML workloads.

Implemented standardized practices for data acquisition, transformation, and analysis using big-data technologies (Databricks, ADF, Glue).

Authored data dictionaries, lineage diagrams, and ETL specifications; established naming standards and governance-by-design.

Delivered CI/CD for pipelines and SQL/DDL with Azure DevOps/GitHub; enforced quality gates and peer review workflows.

Partnered with BI teams and business stakeholders to ensure usability and efficiency of datasets; iterated rapidly on prototypes.

Senior Data Engineer / Data Modeler Accenture – Client Engagements Mar 2019 – May 2020

Designed cloud-native analytics platforms (BigQuery, Dataflow, Pub/Sub) and mapped patterns to AWS/Snowflake equivalents.

Built relational and analytical models; implemented governed ingestion frameworks and curated semantic layers for reporting.

Collaborated with cross-functional teams on requirements; validated prototypes and improved model usability.

Data Modeler / Data Governance Specialist Accenture – Client Engagements Feb 2017 – Mar 2019

Implemented metadata lineage and stewardship workflows using Collibra and Informatica Axon.

Developed optimized Oracle schemas; standardized keys and definitions to support analytics and reporting.

Facilitated design sessions, code reviews, and architecture boards to drive consistency and best practices.

ETL Lead / Data Engineer Accenture – Client Engagements Feb 2015 – Feb 2017

Led ETL development with Informatica PowerCenter and Oracle DW; implemented data quality and audit-ready processes.

Embedded lineage and naming standards in pipeline design; authored comprehensive technical documentation and ERDs.

Coordinated onshore/offshore delivery and sprint planning to maintain delivery timelines and quality.

Education & Certifications

Bachelor of Science – Tribhuvan University, Nepal

Databricks Certified Data Engineer Associate

Databricks Accredited Generative AI Fundamentals

Scaled Agile (SAFe)

Cloud Modernization: Data Warehouse & Data Lake

Leadership & Delivery Highlights

Managed multi-disciplinary delivery (architecture, engineering, QA/UAT) with strong risk management and quality assurance.

Mentored junior modelers/engineers; established review checklists and modeling standards.

Drove continuous improvement—performance tuning, cost optimization, and adoption of modern patterns.

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