Delivering scalable, governed data platforms for analytics and reporting.
I build end-to-end data solutions across Azure and Microsoft Fabric—ingestion, orchestration, modelling, and warehousing— focused on reliability, auditability, and high-quality datasets that teams can trust.
Optional next step: add a PDF CV to this repo (e.g., Mahesh_CV.pdf) and link it from the Contact section.
About
Practical engineering mindset: build once, run reliably, and keep it maintainable.
I am a Data Engineer with strong experience delivering cloud-based data platforms using Azure services (ADF, ADLS, Databricks, Synapse) and Microsoft Fabric. I focus on building dependable ingestion and transformation pipelines, implementing warehouse models for analytics, and improving trust in data through clear controls and governance.
I work confidently with large relational datasets and semi/unstructured formats (JSON, XML, Parquet, CSV), and I collaborate with stakeholders to translate reporting needs into robust, reusable data products.
What this portfolio contains
- Case studies that explain problem context, approach, and outcomes.
- Architecture section showing standard patterns used for production-grade delivery.
- Evidence signals: engineering discipline, quality, and operational thinking.
Tip: Replace placeholders with your real projects; keep it concise and outcome-led.
Capabilities
Skills grouped by delivery capability (easier to assess than a long tool list).
Platforms
Engineering patterns
Languages & DevOps
Case studies
Replace the outcomes with real figures where possible (latency, cost, reliability, adoption).
Modernised Data Platform (Azure / Fabric)
ArchitectureStandardised ingestion and transformation into governed Bronze/Silver/Gold layers for analytics-ready data products.
What I did
- Designed medallion layering and warehouse modelling approach for reporting.
- Built automated ingestion and transformation workflows with clear audit and logging.
- Implemented incremental loads and quality checks to improve trust and reduce rework.
Outcomes (replace with yours)
- Reduced manual reporting effort by standardising curated datasets.
- Improved reliability through automated retries, monitoring, and consistent run history.
- Accelerated onboarding of new sources using reusable pipeline templates.
API Ingestion Framework (Pagination + Resilience)
EngineeringBuilt a repeatable approach for ingesting large REST APIs with stop conditions, retries, and operational auditability.
What I did
- Implemented robust pagination patterns and stop conditions for large datasets.
- Added structured logging, error handling, and run metadata to support supportability.
- Handled schema drift and semi-structured JSON into standardised curated tables.
Outcomes (replace with yours)
- Reduced failures and reprocessing by enforcing idempotent loads.
- Enabled faster troubleshooting through consistent logs and run identifiers.
- Scaled to higher volumes by separating ingestion from transformation layers.
Warehouse Modelling & Performance (SQL)
WarehousingDelivered analytical models and performance-oriented SQL patterns to support complex reporting and downstream BI.
What I did
- Built dimensional structures and curated views for consistent reporting outputs.
- Optimised query patterns (CTEs, indexes, stored procedures) and reduced bottlenecks.
- Implemented history handling patterns (e.g., SCD Type 2) where required.
Outcomes (replace with yours)
- Improved report performance by tuning queries and data structures.
- Reduced logic duplication by centralising definitions in curated layers/views.
- Increased confidence by enforcing consistent business rules.
What to add next (high impact)
Add one diagram per project and a short “Results” line with metrics (duration reduced, costs reduced, reliability improved, adoption increased).
Architecture patterns
Use this section to demonstrate your design thinking. Add diagrams as images later.
Reference pattern (typical)
Then replace this placeholder with an <img> tag.
Operational readiness checklist
- Idempotent loads with clear re-run behaviour
- Retries / backoff for transient failures
- Run metadata: start/end, row counts, status, error details
- Data quality checks at key boundaries (Bronze→Silver, Silver→Gold)
- CI/CD to promote changes safely and consistently
- Documentation and change control
How to add a diagram (quick)
Create /assets folder in this repo, upload a PNG (e.g., architecture.png), then insert:
Experience
Keep this concise. If you want, you can add employer names and dates later.
Data Engineer • Azure / Fabric
Delivered scalable ETL/ELT pipelines across ADF, Databricks, Synapse/Fabric; implemented incremental processing, modelling and quality controls to support complex analytics and reporting.
Data Platform Developer • Warehousing & Integration
Built and maintained relational warehouse structures and T-SQL logic; collaborated with stakeholders to define business rules, improved performance, and operationalised processes with scheduling and documentation.
Optional: add a “Highlights” bullet list with measurable achievements (e.g., % reduction in runtime, cost savings, data quality improvements).
Contact
Make it straightforward to validate your work and get in touch.
Direct
Email: connect@themahesh.org
GitHub: github.com/xxmahesh
LinkedIn: linkedin.com/in/i-mahesh
Optional: add a CV PDF to this repo (example: /Mahesh_CV.pdf) and link it here.
Quick message template
Copy/paste into LinkedIn or email:
Hello, I’m Mahesh. I’m a Data Engineer specialising in Azure and Microsoft Fabric—building governed ingestion pipelines and warehouse-ready models for analytics. I would welcome a short discussion to understand your data platform goals and how I can help.
Custom domain (your WordPress domain)
You can point your domain to GitHub Pages later. When ready, add your domain in GitHub Pages settings and set DNS records at your domain provider.