Azure-first data platform portfolio

Rohit Kumar Bhandari

I design cloud data platforms that are scalable, governed, and ready for enterprise analytics.

Data engineering professional with hands-on delivery across Azure Data Factory, Databricks, DevOps, and analytics pipelines. I am building toward a Microsoft-aligned Data Architect role by focusing on platform thinking, reliable delivery, and business-ready data products.

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Azure stack ADF, Databricks, DevOps, analytics delivery
Architecture focus Data movement, transformation, governance, CI/CD
Target role Microsoft-oriented Data Architecture opportunities
Portrait of Rohit Kumar Bhandari

Current positioning

Azure Data Engineer evolving into Data Architect

  • Designing ingestion and transformation pipelines in Azure
  • Supporting deployment workflows with CI/CD and environment promotion
  • Translating analytics requirements into maintainable cloud solutions
Why this site

Built to communicate architecture readiness

This portfolio is intentionally focused on the signals that matter for a modern Microsoft data role: cloud design judgment, enterprise delivery, governance awareness, and the ability to explain technical decisions clearly.

What I bring

I work at the intersection of data engineering and platform thinking. My background combines hands-on pipeline development with deployment discipline, stakeholder alignment, and a strong bias toward building systems that can be operated reliably at scale.

  • Azure Data Factory orchestration and scheduling
  • Databricks notebooks and transformation workflows
  • CI/CD for data pipelines and notebooks
  • Analytics delivery for business stakeholders
  • Data movement across heterogeneous source systems
  • Collaboration across engineering and business teams
LocationIndore, India
Emailrohitrbzn@gmail.com
FocusAzure Data Architecture
Open toEnterprise data platform roles
StrengthsCloud delivery, communication, ownership
What hiring teams can expect

Signals of impact

These are the qualities I want this portfolio to demonstrate: thoughtful architecture, delivery discipline, and the ability to turn complex requirements into dependable data solutions.

Azure

Cloud-native delivery

Experience building and supporting pipelines using Microsoft-aligned services and practices.

CI/CD

Operational discipline

Deployments and promotion workflows designed to reduce manual overhead and environment drift.

ELT

Reliable transformation

Notebook-driven processing and pipeline orchestration built with maintainability in mind.

Stakeholders

Business alignment

Solutions framed around reporting, analytics, and the practical needs of decision-makers.

Core expertise

Capabilities aligned to a data architecture path

Azure Data Factory 95%
Azure Databricks 88%
Data Pipeline Design 90%
Python for Data Workloads 82%
Azure DevOps and CI/CD 85%
Cloud Analytics Delivery 84%
Stakeholder Communication 87%
Architecture Documentation 80%
Selected architecture work

Case studies framed the way a hiring manager reads them

These examples are written to show thought process, platform choices, and delivery outcomes instead of only listing technologies.

Azure orchestration

Enterprise ingestion and transformation pipelines

Designed and supported data movement workflows that ingested data from multiple sources, standardized transformation steps, and improved delivery reliability for downstream analytics.

  • Used Azure Data Factory to orchestrate ingestion, dependencies, and scheduling.
  • Integrated Databricks notebooks to handle transformation logic and reusable processing steps.
  • Focused on maintainability by separating orchestration concerns from business transformations.
Platform delivery

Deployment-ready data workflows with DevOps alignment

Contributed to delivery pipelines that moved notebooks and orchestration assets across environments with better control, reducing manual deployment friction.

  • Applied Azure DevOps practices to support environment promotion and repeatable releases.
  • Improved confidence in change delivery for data assets that affect production reporting.
  • Built habits around versioning, consistency, and operational ownership.
Analytics enablement

Cloud-hosted analytics solutions for business teams

Worked on analytics and intelligent application workloads where cloud infrastructure, application behavior, and data pipelines had to support business use cases together.

  • Bridged the gap between data preparation, application logic, and stakeholder-facing outputs.
  • Learned to design for usability, not just technical completion.
  • Strengthened communication with teams consuming analytical results.
Career progression

Experience building toward architecture ownership

Professional Experience

Data Engineer

July 2021 - Present

Numantra Technologies, Mumbai

  • Built and supported Azure-based data systems for automation, machine learning, and analytics use cases.
  • Created ADF pipelines for migration, orchestration, and transformation across multiple data sources.
  • Developed Databricks notebooks and integrated them into production-oriented workflows.
  • Helped deploy data assets across environments using Azure DevOps and CI/CD practices.

Data Analyst and Cloud Engineer

July 2020 - June 2021

Analytics Domain, Pune

  • Developed intelligent web and analytics solutions running on cloud infrastructure.
  • Used Python and Django in projects that combined analytics logic with business-facing applications.
  • Deployed workloads on GCP virtual machines with practical production considerations.

Education and Foundation

Training Program in Data Science

2019 - 2020

EXCELR Solutions

Built a practical foundation in analytics, machine learning workflows, and applied problem-solving.

Bachelor of Engineering in Computer Science

2014 - 2018

SGSITS, Indore

Studied cloud computing, artificial intelligence, data structures, and software development fundamentals.

Architecture trajectory

Current focus

I am intentionally moving from implementation-heavy delivery toward higher-level design ownership across data platform architecture, governance, observability, and solution communication.

Next conversation

Let’s talk about Azure data architecture roles

If you are hiring for data engineering or data architecture work, I would be glad to discuss platform design, cloud delivery, and how I can contribute to enterprise-scale analytics systems.

Resume Open resume

Best fit

Roles I am targeting

  • Azure Data Engineer
  • Data Platform Engineer
  • Associate Data Architect
  • Microsoft ecosystem data roles