1. 01.About
  2. 02.Skills
  3. 03.Experience
  4. 04.Work
  5. 05.Blog
  6. 06.Contact
Resume

Hi, my name is

Gnana Aravindh Elavazhagan.

I build large-scale data platforms.

I'm a Senior Data Engineer with 8+ years of experience designing and building scalable, reliable data infrastructure. I specialise in batch and streaming systems that power analytics and machine learning at scale.

Check out my work!

About Me

Hello! I'm Gnana, a Senior Data Engineer based in Chennai, India. I enjoy creating things that live in the cloud and process massive amounts of data. My interest in data engineering started back in 2017 when I realised the power of turning raw data into actionable insights.

Fast-forward to today, and I've had the privilege of working on complex data platforms at companies like PayPal, Ford Motors, and Tredence Analytics — processing terabytes of data daily. My main focus is building reliable, scalable data infrastructure and mentoring engineers on best practices.

Here are a few technologies I've been working with recently:

  • ▹Apache Spark
  • ▹SQL / BigQuery
  • ▹Apache Kafka
  • ▹GCP (Dataflow, GCS)
  • ▹Apache Airflow
  • ▹Python
  • ▹Snowflake
  • ▹Apache Beam
GAE

Skills & Technologies

Languages

PythonSQLShell / Bash

Big Data

Apache SparkApache KafkaHadoopHiveApache Beam

Cloud — GCP

BigQueryDataflowCloud Storage (GCS)Cloud FunctionsPub/SubCloud Monitoring

Databases

PostgreSQLSnowflakeOracleSQL Server

Orchestration & DevOps

Apache AirflowTerraformDockerKubernetesGitCI/CDTekton

Other Tools

Great ExpectationsDatadogInformatica PowerCenter

Where I've Worked

Senior Data Engineer @ Tredence Analytics

December 2024 – Present

  • ▹Architected and scaled GCP-based data platforms using BigQuery, building 100+ pipelines for data ingestion.
  • ▹Built high-performance batch and real-time pipelines with Dataflow, integrating Oracle, Snowflake, Kafka, and GCS — processing millions of events per day.
  • ▹Orchestrated 200+ production workflows using Airflow, achieving 99.9% pipeline reliability with robust monitoring and alerting.
  • ▹Led technical delivery enabling 30–40% faster reporting, improved data quality, and business-ready insights for leadership and operations teams.
  • ▹Mentored 5+ junior engineers on data engineering best practices and code review standards.
  • ▹Drove data governance, performance optimisation, and stakeholder alignment to enable real-time, insight-driven retail decisions.

Staff Data Engineer @ Altimetrik – PayPal

June 2024 – December 2024

  • ▹Spearheaded end-to-end data engineering for 3 key PayPal/Venmo products (Add Funds, Cash Deposits, Plus Customers).
  • ▹Designed scalable data pipelines enabling KPI tracking that contributed to a 20% increase in transaction and deposit volumes.
  • ▹Built and maintained 50+ ETL pipelines supporting analytics teams and business intelligence reporting.
  • ▹Developed and maintained experimentation frameworks for A/B testing — enabling real-time data collection and analysis for UX experiments.
  • ▹Engineered a robust experimentation data infrastructure, driving a 15% increase in conversion rates through data-backed UX decisions.
  • ▹Optimised BigQuery SQL queries and warehouse performance, reducing report generation time by 60%.
  • ▹Implemented CI/CD pipelines using DataLM and Git for reliable, scalable, production-ready data deployments.
  • ▹Collaborated with cross-functional teams to define data requirements and implement solutions.

Data Engineer @ Ford Motors

June 2021 – June 2024

  • ▹Led enterprise-scale migration of historical data from Hive to GCP BigQuery using Hadoop DistCp and GCS connectors.
  • ▹Built scalable real-time and batch ingestion pipelines using Apache Kafka, Dataflow (Apache Beam), and BigQuery for advanced analytics.
  • ▹Developed streaming Dataflow jobs for cleansing, transformation, and real-time enrichment before warehouse storage.
  • ▹Optimised BigQuery performance using partitioning, clustering, ARRAY<STRUCT> modelling, and fixed slot reservations to reduce cost.
  • ▹Implemented deduplication using BigQuery scheduled queries and key-based logic to improve data accuracy.
  • ▹Led data governance initiatives ensuring GDPR, HIPAA, and CCPA compliance — implementing PII tagging, views, and access control policies.
  • ▹Automated infrastructure using Terraform, Git version control, and Tekton-based CI/CD pipelines for scalable GCP deployments.
  • ▹Established monitoring and alerting using Cloud Monitoring & Logging for reliable real-time data processing.
  • ▹Developed an LLM-powered chatbot using LangChain, Python, and Flask to enhance customer query automation and support operations.

ETL Developer @ Tata Consultancy Services

October 2017 – June 2021

  • ▹Designed and developed 50+ ETL mappings and workflows in Informatica PowerCenter to extract, transform, and load data from Oracle, SQL Server, and flat files into the enterprise data warehouse.
  • ▹Implemented Slowly Changing Dimensions (SCD Type 1 & 2) for tracking historical data changes across customer and product dimensions.
  • ▹Built complex PL/SQL stored procedures and shell scripts for data transformation, cleansing, and reconciliation.
  • ▹Optimised Oracle SQL queries and ETL workflows, improving overall pipeline performance by 35% and reducing batch processing windows.
  • ▹Migrated legacy batch jobs to Informatica PowerCenter, eliminating manual processing steps and reducing errors by 70%.
  • ▹Worked with Hadoop ecosystem (HDFS, Hive) for batch analytics and large-scale data processing.
  • ▹Provided production support including on-call coverage, incident root cause analysis, and SLA adherence for critical data pipelines.
  • ▹Collaborated with business analysts and DBAs to translate data requirements into technical specifications and physical data models.

Some Things I've Built

Featured Project

Real-Time Streaming Data Platform

An end-to-end streaming pipeline ingesting events from Kafka, transforming them via Cloud Dataflow (Apache Beam), and landing into BigQuery with exactly-once semantics. Handles 100K+ events/sec with automatic autoscaling and sub-second latency.

  • Apache Kafka
  • Cloud Dataflow
  • Streaming
  • BigQuery

Other Noteworthy Projects

Oracle to BigQuery Batch Pipeline

Enterprise batch ingestion pipeline extracting data from Oracle, transforming via Dataflow, landing in GCS, and loading into BigQuery for analytics-ready datasets.

  • Oracle
  • Dataflow
  • GCS
  • BigQuery

Snowflake to BigQuery Data Pipeline

Scheduled data sync pipeline moving curated Snowflake datasets through Dataflow into GCS staging and BigQuery, enabling unified reporting across cloud platforms.

  • Snowflake
  • Dataflow
  • GCS
  • BigQuery

Writing & Insights

I write about data engineering, system design, lessons learned from production systems, and best practices I've discovered along the way.

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What's Next?

Get In Touch

I'm currently open to new opportunities and interesting data engineering challenges. Whether you have a question, want to discuss a project, or just want to say hi — my inbox is always open!

Say Hello