Looking for a job

M J Darad

@mjdarad

Message

Senior Data Engineer driving scalable, cost‑efficient cloud data platforms and analytics.

Bangladesh

Message

What I'm looking for

I seek a senior data engineering role focused on cloud-native data platforms, automation, observability, and delivering fast, cost-efficient analytics while mentoring teams.

I am a Senior Data Engineer with proven success designing cloud-native data warehouses, lakes, and orchestration systems that accelerate analytics and reduce costs. I architect pragmatic solutions on AWS (Redshift, S3), build robust ETL pipelines, and automate operations to improve reliability and performance.

My recent work includes replacing transactional databases with a Redshift-based data warehouse to deliver 3× faster analytics, building a cost-optimized S3 data lake, developing 120+ Airflow DAGs, and creating an internal RAG-powered Data Warehouse assistant adopted across multiple departments. I author PL/SQL procedures, prototype Parquet ingestion, and implement lifecycle/purging systems to control storage and compute spend.

I thrive on solving large-scale data challenges, improving time-to-insight, and enabling data-driven teams through automation, observability, and clean architecture. I bring hands-on expertise in pipeline engineering, data migration, monitoring frameworks, and integrating CI/CD for data workflows.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

NEXT Ventures

Feb 2025 - Present (7 months)

Architected and implemented a cloud-native Redshift data warehouse and S3 data lake, delivering 3× faster analytics and reducing infrastructure/storage costs by ~$585/$400–$600 per month; built 120+ Airflow DAGs and a RAG-powered internal assistant that cut SQL backlog ~40% and ad-hoc reporting from days to hours.

Amazon Redshift AWS S3 Apache Airflow Python fastAPI Pgvector CI CD

Specialist, Data Engineering

Robi Axiata Limited

Aug 2022 - Jan 2025 (2 years 5 months)

Maintained and optimized ETL pipelines processing up to 50B records/day and systems for 54.5M users; led migration of 400M records between Oracle and Cloudera using Sqoop and implemented centralized monitoring to improve SLA observability.

Hive Hadoop Sqoop Pl SQL Shell Scripting ETL Data Warehousing Monitoring Oracle

Machine Learning Engineer

Markopolo.ai

Mar 2021 - Jun 2022 (1 year 3 months)

Developed enterprise NLP solutions for brand monitoring and sentiment analysis, implementing intent recognition with transformer models and building large-scale web scraping and preprocessing pipelines for client analytics.