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Data Engineer résumé example

Data Engineer résumé example — mid level, written for India, set in the Terminal design
Data Engineer résumé example, in the Terminal design

Six years of pipeline work in Indian fintech, including a contract stint, measured in freshness, cost and failed runs.

Written for India, at mid level, in the data & analytics field, shown in the Terminal design. Every name, employer, address and number below is invented.

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Rohit Nambiar — Data Engineer

Bengaluru, Karnataka

Data engineer with six years on high-volume financial and marketplace pipelines. Runs the ingestion layer behind 2.1 billion daily transaction events, and rebuilt a ledger reconciliation that took 14 hours into a stream that settles in nine minutes.

Experience

Data Engineer, Arclane Financial Technologies · February 2023 – present

Platform team of eleven. Owns ingestion, the lakehouse and the data-quality gate in front of reporting.

  • Owns the ingestion layer landing 2.1 billion transaction events a day; p95 freshness stayed under seven minutes through a threefold rise in volume.
  • Replaced a nightly bulk dump with change-data capture, cutting the ledger reconciliation window from 14 hours to nine minutes.
  • Cut cluster spend ₹48 lakh a year by moving 70 Spark jobs to autoscaling clusters and rewriting the three that shuffled 4 TB needlessly.
  • Built the quality gate that blocks a bad partition before reporting sees it; it has caught 61 upstream schema changes in 18 months.

Data Engineer (contract), Orenda Marketplace · April 2022 – January 2023

Ten-month fixed-term contract to stand up a first data warehouse.

  • Delivered the first warehouse covering orders, payouts and sellers in nine months as the only data engineer on a four-person team.
  • Wrote the orchestration framework the client still runs: 180 pipelines generated from YAML, with backfills that cost one command instead of a day.

Associate Data Engineer, Auralite Analytics · July 2020 – March 2022

  • Built the nightly ETL for a media measurement product, processing 400 GB of viewership logs inside a 90-minute window.
  • Took the nightly pipeline from one failed run in five to one in forty by making writes idempotent and retries safe to repeat.

Education

  • B.Tech. Computer Science and Engineering · National Institute of Technology Karnataka, Surathkal · July 2016 – May 2020

Skills

  • Languages: Python, Scala, SQL, Bash
  • Platform: Spark, Kafka, Databricks, Airflow, AWS, Delta Lake
  • Pipelines: Change data capture, Debezium, dbt, Data quality gates, Backfills
  • Practice: Cost tuning, Schema evolution, On-call, Runbooks

Certifications

  • AWS Certified Data Engineer – Associate — Amazon Web Services
  • Databricks Certified Data Engineer Professional — Databricks
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