4 years 9 months
Years of experience delivering enterprise data engineering outcomes.
Enterprise Data Engineering
Building Reliable
Enterprise
Data Platforms.
I modernize legacy ETL systems, optimize enterprise-scale data pipelines and build scalable cloud-native data solutions.
Focused on production reliability, enterprise modernization, and cloud-ready platform engineering for high-volume data environments.
Engineering Snapshot
A quick view of enterprise delivery scale.
This section gives recruiters an immediate summary of the scale, ownership, and modernization focus behind the portfolio.
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10+ TB / Day
Operational exposure to high-volume data movement and processing environments.
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20+ Pipelines
Enterprise ETL pipelines supported, optimized, or modernized for production use.
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Production SME
Trusted with stability, issue resolution, and dependable delivery in live systems.
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Cloud Modernization
Focused on moving critical workloads toward scalable and maintainable cloud data patterns.
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Performance Optimization
Improves throughput, reliability, and operational efficiency across enterprise data workflows.
Engineering Philosophy
Simplifying enterprise systems through reliable execution.
Akash approaches data engineering as a systems discipline. The goal is not simply to move data from one point to another, but to reduce operational complexity, improve delivery confidence, and create a platform foundation that teams can trust.
His work is centered on outcomes: stabilizing production workflows, modernizing legacy ETL patterns, enabling cloud transition, and making large-scale data operations easier to maintain, support, and extend.
- Design for reliability before complexity.
- Modernize with business continuity in mind.
- Improve systems in ways that reduce long-term operational friction.
Professional Experience
Experience shaped by enterprise delivery, platform ownership, and modernization work.
This section highlights the delivery areas that define Akash’s profile across enterprise ETL operations, modernization, cloud-readiness, and platform reliability.
Enterprise ETL Operations and Support
Supported enterprise ETL workflows in high-volume environments where operational stability, recovery speed, and dependable data availability directly affected downstream reporting confidence.
Key technologies: Ab Initio, IBM DataStage, SQL, Teradata
Legacy ETL Modernization
Improved established ETL ecosystems by reducing workflow complexity, strengthening maintainability, and supporting modernization paths that better align with scalable enterprise delivery.
Key technologies: Python, SQL, Azure Data Factory, Databricks
Cloud Migration Readiness
Contributed to cloud-readiness efforts by improving transformation efficiency, aligning data workflows to modern platform patterns, and supporting migration direction for evolving enterprise workloads.
Key technologies: Azure, Azure Data Factory, Databricks, Spark
Performance and Reliability Ownership
Improved execution consistency by prioritizing throughput, issue prevention, and resilient pipeline behavior in environments where reliability and supportability matter as much as raw performance.
Key technologies: SQL, Spark, PostgreSQL, SQL Server
Engineering Highlights
The strengths that make Akash valuable in enterprise data teams.
These highlights summarize how Akash approaches modernization, scale, delivery reliability, and long-term platform thinking.
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Modernization with Practical Discipline
Modernizes legacy ETL environments with care, improving future readiness without introducing avoidable delivery risk into production systems.
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Enterprise Scale Mindset
Approaches data engineering with an understanding that every pipeline decision influences downstream reporting, support effort, and execution confidence at scale.
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Cloud-Oriented Data Platform Thinking
Builds toward cloud-aligned data platforms that improve maintainability, extensibility, and long-term architectural flexibility.
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Production Reliability Focus
Treats reliability as a primary engineering outcome, especially in data systems that support business-critical processes and decision-making.
Technology Stack
Tools and platforms that support enterprise-grade data engineering delivery.
The stack below reinforces technical depth while keeping the focus on engineering outcomes and platform capability.
Programming
- SQL
- Python
- PySpark
Cloud
- Azure
- Azure Data Factory
- Databricks
Big Data
- Spark
- Hive
- Hadoop
Databases
- Teradata
- PostgreSQL
- Netezza
- SQL Server
ETL
- IBM DataStage
Reporting
- Power BI
- Tableau
Resume
Download the resume for a deeper view of delivery experience and role alignment.
For recruiters evaluating enterprise data engineering, ETL modernization, or Azure-focused data platform roles, the resume provides a direct view of delivery scope, technical depth, and production-facing ownership.
Contact
Let's Build Something Great Together.
Interested in discussing enterprise data engineering, cloud modernization or new opportunities?
I'd love to hear from you.
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Location Chennai, India - LinkedIn Open profile
- Email connect@akashkarthik.in