Amar Deshmukh

Senior Data Engineer with 17+ years across enterprise data platforms, finance operations, stakeholder-facing delivery, technical analysis, ETL/data lake engineering, production support, and functional domains including transfer agency, asset servicing, regulatory reporting, and General Ledger. Currently building hands-on Databricks, DBT, and Snowflake-aligned project evidence.

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Featured project

Mutual Fund Data Engineering Platform

A verifiable portfolio case study for Perth, Australia data engineering roles, showing practical Azure SQL, Databricks Lakehouse, DBT, dimensional modelling, and dashboard delivery.

Source

Blob Landing and Azure SQL

CSV source files land in Azure Blob Storage, then load into normalized investor, agent, fund, transaction, commission, holding, asset, and price tables.

Lakehouse

Databricks Delta Lake

Bronze, Silver, and Gold layers with incremental processing, quality checks, time travel, and optimized serving tables.

Analytics

DBT and Dashboard

Facts and dimensions powering commission, holdings, fund flows, AUM, and reconciliation views.

Role alignment

Enterprise Data Engineering, Integration, And Operations

Technical delivery

Data Platforms And Pipelines

Experience across SQL Server, Informatica, SSIS, Ab-Initio, Kafka/MQ, Kudu, data lake hydration, EOD/intraday reporting, automation, and performance tuning.

Stakeholders

Bridge Between Business And Engineering

Strong record translating operational requirements into technical designs, coordinating global teams, managing UAT, documenting delivery, and supporting production outcomes.

Functional depth

Finance To Trading-Style Data

Domain background in investor/fund transactions, holdings, commissions, FATCA/CRS, General Ledger, reconciliations, and valuation-style reporting patterns.

Architecture diagram

Operational Source To Reporting Flow

The platform starts with generated CSV files landing in Azure Blob Storage, loading into Azure SQL OLTP, then flowing through Databricks Delta Lake medallion layers before DBT publishes facts and dimensions for reporting.

AADData mutual fund data platform architecture with Blob Storage landing, Azure SQL, Databricks, DBT mart tables, and web reporting
Download architecture SVG

Data warehouse schema

Facts And Dimensions Model

The warehouse model is designed for daily and monthly mutual fund reporting, with conformed dimensions shared across transaction, commission, holding, fund flow, and AUM facts.

Mutual fund data warehouse star schema with dimensions and fact tables
Download warehouse schema SVG

Experience signal

17+ Years Across Finance Data Delivery

17+

Years delivering data engineering, ETL, data warehouse, data lake, and reporting solutions.

Finance

Transfer agency, asset servicing, investor/fund holdings, commissions, FATCA/CRS, and General Ledger exposure.

Cloud

Azure data engineering foundation expanded through Databricks and Snowflake certification practice.

Certifications and active learning

Architecture, Azure, Data Warehousing, And Investment Domain

In progress

Hands-On Upskilling

  • Databricks Data Engineer Associate
  • SnowPro Core
  • DBT project implementation through this portfolio build

Cloud and data

Microsoft And Snowflake

  • Microsoft Certified Azure Data Engineer Associate - DP-203
  • AZ-900 Azure Fundamentals
  • Snowflake Hands-On Essentials: Data Warehouse
  • Microsoft Certified Professional
  • 70-461 Querying Microsoft SQL Server 2012
  • 70-463 Implementing Data Warehouse with Microsoft SQL Server 2012

Architecture and domain

Enterprise And Functional

  • TOGAF Enterprise Architecture Practitioner
  • TOGAF Foundation
  • BNY AI Foundation
  • BNY AI Builder
  • PMI Certified Associate in Project Management
  • Chartered Institute for Securities and Investment, UK - Investment Administration Qualification

Contact

AMARDESHM@GMAIL.COM - +91(0)8411001200