Rahul Kumar Dhakar

About Rahul Kumar Dhakar

Results-driven Data Engineer with 5+ years of experience in designing and delivering scalable, cloud-native data solutions. Proficient in PySpark, Python, SQL, and AWS cloud platform, with deep expertise in ETL/ELT pipeline development and data warehousing. Skilled in data modernization initiatives to enable advanced analytics and empower data scientists with reliable, high-quality, and accessible dataCurrently functioning as a Lead Data Engineer at Tata Consultancy Services (TCS) for a global pharmaceutical client, responsible for end-to-end data engineering—spanning data ingestion, transformation, orchestration, and system optimization. Skilled in building modular, reusable pipeline components, and aligning engineering solutions with business goals using cloud-native servicesAdditionally, responsible for ensuring data quality and compliance with organizational and regulatory standards based on risk categorization, due to the sensitive nature of Real-World Evidence (RWE) data such as Claims, HEOR, Registries, and Wearable datasets. Actively mentoring junior developers on engineering best practices and maintaining comprehensive SOPs and quality documentation to ensure adherence to governance standards and streamline development processesSkills:Data Engineering : PySpark, Python, SQL, Snowflake, AirflowAWS : Lambda, Glue, Redshift, RDS, Fsx, S3, Cloud Formation, IAM, EventBride, CloudWatch, CICD Pipelines, EC2Tools & Platforms: Jira, ServiceNow, GitHub

Experience

1. IT Analyst

   Tata Consultancy Services

   Feb 2020 — Present · IN

   Orchestrated scalable AWS Glue Spark ETL pipelines using multithreading to process 10M+ de-identified records across 150+ tables, transforming data from S3 and loading into Redshift. Engineered SCD2 using Delta Lake for efficient version control, ensuring seamless integration with downstream business applications. Streamlined high-volume healthcare data workflows through parallel processing and optimized resource utilization. Led AWS cloud migration (FSx, Lambda, S3) and Salesforce integration, automating end-to-end data workflows to replace on-prem infrastructure, reducing costs by 40% Streamlined study artifact handling via event-driven architecture (SAS or any analytical tool → FSx → S3 → Salesforce) with real-time updates, email alerts, and post-study cleanup. Designed and implemented Glue DQDL-powered data quality frameworks for AWS Redshift, cutting manual workload by 80% and ensuring compliance through automated error detection and stakeholder-facing dashboards. Built an end-to-end pipeline with AWS AppFlow and Airflow to incrementally extract 70+ Salesforce objects, load to S3, and transform into Redshift, streamlining data workflows and eliminating manual intervention Integrated ServiceNow with existing and new pipelines to enable real-time failure alerts and automated error monitoring, reducing support team resolution time by 30%. Designed and orchestrated a Redshift-to-Salesforce data pipeline using AWS Glue triggers and custom API integration, enabling scheduled automation for seamless data synchronization. Spearheaded end-to-end AWS infrastructure setup (Glue, Lambda, IAM policies etc.) via CloudFormation, automating deployments to ensure scalability, security, and streamlined team operations. Designed and orchestrated a Redshift-to-Salesforce data pipeline using AWS Glue triggers and custom API integration, enabling scheduled automation for seamless data synchronization.

Education

• Lovely Professional University

  Bachelor of Technology, Information Technology

  2015 — 2019