Aravind V.K. / Technology & AI

Data engineering.
Cloud architecture.
AI & innovation.

A senior technologist working across data engineering, cloud architecture, distributed systems and automation, with nearly two decades in software engineering.

Tech Lead / Data Engineer · Huntington National Bank

Explore the experience UI & API automation AI & innovation
Illustrative architecture patterns

A dependable path from source to insight.

  1. 01

    Sources

    Applications · files · databases

  2. 02

    Ingest

    Extract · validate · stage

  3. 03

    Process

    Spark · SQL · orchestration

  4. 04

    Serve

    Warehouse · analytics · APIs

Data qualityAccess controlObservabilityRepeatable delivery
Nearly two decades
Software engineering
GCP & AWS
Cloud platforms
Data & APIs
Enterprise architecture
Technical leadership
Automation & production systems

Professional identity

Enterprise experience.
Engineering depth.

Data EngineerCloud ArchitectAPI & Platform EngineerTechnical Lead

Aravind V.K. works at Huntington National Bank as a Tech Lead / Data Engineer, contributing to data engineering initiatives, platform modernization and architecture decisions for production systems.

His professional journey spans banking, healthcare, telecommunications, energy and consulting. A foundation in Java and enterprise application development has grown into work with distributed data platforms, cloud infrastructure, APIs and automation.

Across these disciplines, the focus is practical: systems that can scale, workflows that engineers can maintain, and architecture that supports reliable delivery over time.

Architecture & engineering

From data movement
to dependable platforms.

Technical experience across the foundations of enterprise systems: cloud infrastructure, distributed processing, service interfaces and the workflows that keep delivery consistent.

Cloud & data platforms

Reliable cloud foundations for analytics, data movement and long-term scale.

GCP and AWS platforms, with BigQuery, Dataproc, Dataflow and Cloud Storage across the data lifecycle.

Distributed data engineering

Dependable processing systems for large datasets and high-volume data flow.

Hadoop, Hive and Spark for distributed processing, with Kafka and Sqoop connecting data across systems.

APIs & microservices

Modular service platforms that connect applications and remain maintainable in production.

Java, Spring Boot and REST APIs, supported by experience in enterprise backend development.

Automation & platform engineering

Repeatable engineering workflows that reduce manual effort and support consistent delivery.

Playwright and Cucumber for UI/API test automation; Azure DevOps, Jenkins and CI/CD alongside Terraform, Python and Unix shell automation.

Warehousing & pipelines

Clear pipeline and warehouse design that turns source data into useful analytics.

SQL-based data work across BigQuery, Oracle, Teradata, SQL Server and MySQL.

Security & cloud governance

Security, access and governance considered as part of the architecture from the start.

IAM and governance automation that bring consistency to cloud operations and platform access.

Current work / Test automation

UI & API automation.
Playwright + Cucumber.

Building a fully automated UI and API testing application around Playwright and Cucumber.

Current work brings together behaviour-driven scenarios, browser and API testing, delivery tooling and AI-assisted development. Azure DevOps (ADO) and Jenkins are part of this ongoing automation work.

Cucumber

Behaviour-driven scenarios

Readable scenarios connect expected application behaviour with executable checks, giving UI and API automation a common language.

Playwright

Automated UI & API testing

Browser interactions and API checks are brought into one testing application, with repeatable execution at the centre of the design.

Azure DevOps · Jenkins

Delivery & engineering workflow

Automation extends into the delivery workflow, alongside GitHub Copilot, Claude, OpenCode and AI-assisted Playwright development.

Platforms, languages & tools

A broad technical toolkit.

From Java and enterprise applications to cloud-native data platforms, UI/API automation and AI-assisted engineering. Codex and MCP exploration are detailed in the AI section below.

Cloud platforms

  • GCP
  • AWS EMR
  • Dataproc
  • BigQuery
  • IAM
  • Cloud Storage

Data engineering

  • Hadoop
  • Hive
  • Spark
  • Kafka
  • Sqoop
  • Dataflow

Programming

  • Java
  • Python
  • SQL
  • Unix Shell

Frameworks & APIs

  • Spring Boot
  • Microservices
  • REST APIs

Infrastructure & automation

  • Terraform
  • Azure DevOps (ADO)
  • Jenkins
  • CI/CD pipelines

Databases

  • MySQL
  • Oracle
  • Teradata
  • SQL Server

UI & API test automation

  • Playwright
  • Cucumber
  • UI testing
  • API testing

AI-assisted engineering

  • GitHub Copilot
  • Claude
  • OpenCode
  • AI-assisted Playwright workflows

Professional journey

A career across
enterprise technology.

Experience across banking, healthcare, consulting and software development. AEP, Verizon and Catalist engagements were part of the Compunnel consulting role.

  1. Current role

    Huntington National Bank

    Tech Lead / Data Engineer

    Guiding data engineering initiatives, platform modernization and architecture choices for production systems.

    • Data engineering
    • Platform modernization
    • Technical leadership

  2. Compunnel Software Group

    Data Architect / Data Engineer · Consulting

    Consulting engagements for AEP, Verizon and Catalist, focused on scalable architecture and reliable data pipelines.

    • Data architecture
    • Enterprise consulting
    • Pipeline engineering

  3. Cardinal Health

    GCP / Big Data Tech Lead

    Building cloud-native data platforms on GCP with attention to reliability, performance and governance.

    • Google Cloud
    • Big data
    • Cloud governance

  4. Johnson & Johnson

    Big Data Developer

    Developing distributed data solutions supporting analytics, reporting and large-scale processing.

    • Distributed processing
    • Analytics
    • Enterprise data

  5. TCS

    Hadoop Developer

    Working across Hadoop ecosystem components and batch-processing pipelines for enterprise data programs.

    • Hadoop ecosystem
    • Batch processing
    • Data pipelines

  6. Ideas and Innovations

    Software Specialist

    Delivering software solutions across application workflows with a focus on quality and maintainability.

    • Application development
    • Software quality
    • Maintainability

  7. Vitalect / Gigasphere

    Software Developer

    Contributing to core application modules, data handling and day-to-day production support.

    • Application modules
    • Data handling
    • Production support

  8. Kran Consulting

    Java Developer

    Building Java services and backend components that formed a foundation in enterprise development.

    • Java
    • Backend services
    • Enterprise development

Artificial intelligence

AI & data innovation.

Hands-on AI-assisted engineering, with GitHub Copilot, Claude and OpenCode in the development workflow and AI-assisted Playwright work in test automation.

The data-engineering foundation remains central: useful context, dependable data, maintainable interfaces and repeatable workflows. Alongside current tools, Aravind is exploring Codex and the Model Context Protocol (MCP).

Data engineeringCloud architectureAPIs & automationProduction reliability

In use

AI-assisted development

Working with coding assistants as part of software engineering and automation, with engineering judgment guiding the result.

  • GitHub Copilot
  • Claude
  • OpenCode
  • AI-assisted Playwright

Exploring

Coding agents & connected tools

Exploring Codex and MCP, including how AI applications can work with tools and relevant context. This is an active area of learning and experimentation.

  • Codex
  • Model Context Protocol (MCP)

Across disciplines

Technology in creative practice

AI and technology are also used extensively in filmmaking, videography and mentalism, connecting engineering curiosity with visual storytelling and creative experimentation.

Technical leadership

Architecture is only useful
when teams can build on it.

Reliability, maintainability and clear delivery practices connect the technology choices with the people operating the systems.

Architecture that lasts

Architecture decisions that support long-term maintainability across distributed data ecosystems, with attention to reliability and the demands of production.

Engineering with less friction

API and automation initiatives that bring consistency to engineering workflows and reduce the manual work around delivery.

Governance built into the platform

Cloud governance automation that makes access and operations more predictable, with security considered throughout the design.

Beyond engineering

The logic behind the wonder.

Data engineering teaches precision, structure and respect for detail. Algorithmic thinking shapes how Aravind studies patterns, decisions and attention.

Those instincts carry into filmmaking and live performance through timing, experimentation and visual storytelling. Extensive use of AI and technology connects the engineering and creative practices, from Google Flow and Veo 2 in videography to creative exploration in mentalism.

About Aravind Filmmaking Magic & mentalism

Engineering & professional connections

Let’s talk technology.

Data platforms, cloud architecture, automation and AI.

Start a conversation Contact Aravind