Hemavinothini Arumugam
Data Quality Engineer / Data Engineer with 6+ years of IT experience, including 2+ years focused on enterprise data engineering using Azure Databricks, PySpark, Apache Spark, Delta Lake, Azure Data Factory, SQL, and Snowflake. Experienced in processing 10TB+ daily data from SAP, Oracle, Parquet, CSV, and JSON sources, building scalable ETL/ELT pipelines, implementing validation and standardisation logic, and enabling downstream reporting through Tableau and React dashboards.
Strong fit for Databricks-based data quality initiatives involving automated checks, monitoring, exception reporting, remediation support, and governance-aligned data delivery. Based in the UAE on a Dependent Visa and available to start within 2 weeks.
Experience
Data Engineer & Full Stack Developer
Capgemini
Engineered production-grade ETL/ELT pipelines on Azure Databricks using PySpark and Spark SQL, improving data visibility across business units by 40%.
Built Azure Data Factory and Python workflows to ingest and process 10TB+ of daily enterprise data from SAP, Oracle, Parquet, CSV, and JSON sources.
Implemented transformation, standardisation, validation, and enrichment logic to improve data completeness, consistency, and readiness for analytics and reporting teams.
Designed reusable data quality checks for schema conformance, null handling, duplicate identification, business-rule validation, and source-to-target reconciliation before downstream consumption.
Used Delta Lake and Parquet-based processing patterns to support reliable lakehouse transformations, scalable storage, and repeatable batch processing.
Created exception outputs and reporting-ready datasets to help business and technical teams identify, investigate, and remediate data issues.
Integrated Snowflake as a cloud data warehouse layer, supporting dimensional models, historical analysis, and fast analytical query performance.
Developed RESTful APIs in Node.js and .NET Core to expose curated datasets to dashboards, reporting tools, and frontend applications.
Built CI/CD pipelines using GitHub Actions to automate testing and deployment of data pipeline and application code.
Collaborated with stakeholders to convert business requirements into data models, pipeline specifications, validation rules, and reporting outputs.
Received multiple Capgemini performance awards from 2021 to 2025 for reliable delivery and high-quality engineering solutions.
Full Stack Developer
Greypath
Trichy, India
Implemented database validation logic and query optimisation strategies, improving application query performance by 25%.
Designed and maintained PostgreSQL schemas to support transactional data, reporting requirements, and application data integrity.
Built REST APIs and real-time integrations using Socket.io, enabling reliable data exchange between backend services and frontend dashboards.
Developed responsive, data-driven dashboards and visualisations using React.js to surface operational insights for end users.
Worked in cross-functional Agile teams with analysts, backend engineers, and business users to deliver data-focused application features.
Education
Shri Angalamman College of Engineering
B.E.
Trichy, India
Skills
KEY PROJECTS
Workforce Planning Data Platform
PySpark, Databricks, Snowflake, PostgreSQL, Python, Tableau, React.js, .NET Core
Designed and maintained ETL pipelines to ingest and transform HR and workforce data from multiple sources through Databricks and Snowflake.
Implemented PySpark aggregation and target-estimation logic to process actuals, support planning forecasts, and enable scenario-based cost projections.
Modelled dimensional structures in Snowflake and PostgreSQL to support historical trend analysis and reporting performance.
Applied data standardisation and validation logic before publishing curated datasets to Tableau dashboards and a React.js reporting interface for HR and finance stakeholders.
Continuous Monitoring & Alerting System
PySpark, PostgreSQL, NestJS, React.js
Designed a PySpark-based processing backend to ingest operational metrics, evaluate threshold rules, and trigger near-real-time alerts.
Built data pipelines feeding PostgreSQL stores while maintaining schema integrity and query performance for live monitoring use cases.
Developed REST APIs connecting validated data outputs to a React.js monitoring dashboard, supporting 99.9% service uptime.
CERTIFICATIONS & ACHIEVEMENTS
Microsoft Azure Fundamentals - AZ-900
DP-700: Microsoft Fabric Analytics Engineer
In Progress