Open to Senior & Lead Roles

Namratha
Putrev

Technical Lead  ·  Data Platform Architect  ·  Data Engineer

7 years building scalable data platforms and distributed systems — from fault-tolerant Kafka streams to high-throughput Spark pipelines. Passionate about real-time architectures, clean design, and dependable systems that teams can rely on.

7+
Years Experience
5
Companies
7
Clients Served
About Me

Technical Lead. Architect. Collaborator.

I'm a Technical Lead and Data Platform Engineer with 7 years of professional experience, with a strong focus over the past few years on building and operating scalable data platforms and distributed systems.

My work centres on data engineering, real-time streaming, and cloud-based data architectures — using technologies such as Apache Kafka, Spark, Python, Scala, Databricks, Snowflake, Airflow, Elasticsearch, and SQL/NoSQL databases. I've contributed to both batch and near real-time data initiatives, taking ownership of design, implementation, and production stability within larger platform teams.

In recent roles, I've improved the reliability and performance of streaming pipelines — implementing fault-tolerant processing, offset reconciliation, and low-latency ingestion. These efforts helped increase throughput by up to 50%, support 1,500+ records per minute, and reduce production incidents. I've also modernised data workflows and migrated pipelines to newer platforms, delivering measurable performance improvements.

I value learning from others, mentoring when possible, and collaborating with diverse teams to build dependable data systems. Let's connect to exchange ideas around data engineering, streaming systems, and platform development.

Outside of Work
🌿 Nature & Outdoors ✈️ Exploring Cultures 📚 Reading 🤝 Mentoring 🌍 Travel
Scalable Data Pipeline — Architecture Approach
📥
Data Sources
APIs · Databases · Event Streams · Files
Ingestion Layer
Apache Kafka
Partitioned topics · Multi-namespace · Retention
NRT
Stream Processing
🔄
Spark Structured Streaming
Micro-batches · Checkpointing · Fault-tolerant
1,500+ rec/min
Validation
Integrity & Reconciliation
Offset checks · Row counts · Checksums
Storage
🗄️
Databricks / Snowflake
Delta Lake · Elasticsearch · Object Storage
Orchestration
🎛️
Airflow / AutoSys DAGs
Parallel execution · Monitoring · Alerting
50% faster deploys
By the Numbers

Measurable Impact

1,500+
Records per minute in NRT ingestion pipeline — exceeding throughput NFRs by 50%
50%
Reduction in deployment time via DAG-level Spark configuration support
25min
Per-job processing time saved after migrating pipelines to Databricks
1,800+
Engineering hours saved annually across 5M-record batch jobs
Career

Work Experience

McKinsey & Company
Jan 2024 – Present
📍 Bangalore
Technical Lead
Global management consulting firm
  • Technical Lead for ETL, IETL & Near Real-Time data platform serving ~7 clients, owning architecture, delivery, and production stability.
  • Designed and implemented a high-throughput NRT ingestion pipeline via Apache Kafka + Spark Structured Streaming, achieving 1,500+ records/min — exceeding throughput NFRs by 50%.
  • Architected fault-tolerant streaming with checkpointing, micro-batch offset tracking, and asynchronous progress logging for automated recovery and low-latency processing.
  • Designed an event-driven NRT shutdown pipeline to gracefully terminate streaming jobs, preventing data loss and ensuring consistency.
  • Implemented validation with offset reconciliation, row counts, and checksum verification for end-to-end data integrity.
  • Designed a scalable relationship graph-building algorithm using JGraphT, resolving Spark executor memory issues on large clusters.
  • Managed migration of competitor history from dual to single scorecard architecture, including Elasticsearch analysis, data mapping, and phased execution.
  • Migrated SBM & SC1 pipelines from in-house Spark to Databricks with Airflow DAG integration — 25 min saved per job (~1,800+ hrs/year) across 5M-record batches.
  • Introduced DAG-level Spark configuration support, reducing deployment time by 50% and improving NRT workload flexibility.
  • Owned P0–P3 production incident resolution: root cause analysis, Spark tuning, and SLA adherence across clients.
  • Mentored engineers and led design & code reviews, establishing best practices in streaming and production readiness.
ThoughtWorks
Jan 2022 – Dec 2023
📍 Bangalore
Senior Consultant, Developer
Global software consultancy
  • Led data architecture design sessions to build scalable pipelines using data lake and warehouse patterns.
  • Performed data analysis on large, multi-format datasets to enable business-critical decision making.
  • Improved Databricks job performance via PySpark optimizations, reducing overall processing cost.
  • Developed and maintained ETL pipelines ingesting, transforming, and loading data from heterogeneous sources.
  • Championed TDD and SOLID principles; mentored junior engineers through code reviews and technical huddles.
ONX Technologies
Oct 2020 – Jan 2022
📍 Bangalore
Software Development Engineer
Technology solutions provider
  • Built backend services and APIs using JavaScript, TypeScript, PostgreSQL, and MongoDB.
  • Refactored legacy codebases to improve maintainability and performance at scale.
  • Coordinated across development and testing teams for high-performance, scalable solution delivery.
Katerra Technologies
Apr 2019 – Oct 2020
📍 Bangalore
Software Developer
Technology & construction firm
  • Delivered and maintained scalable system architecture for high-availability applications across the SDLC.
  • Built event-driven microservices-based applications; contributed code fixes and enhancements for production releases.
Ruca Tech Data Science
Aug 2018 – Apr 2019
📍 Bangalore
Software Developer
Data science & technology firm
  • Generated automated reports with Python and Mailgun; integrated email templates using HTML, CSS, and JavaScript.
  • Worked with AWS SageMaker, Amazon Lex, and Amazon Polly for ML-adjacent workflows.
  • Contributed to Python codebases for automated deployment across virtual machines.
Expertise

Technical Skills

Languages
PythonScalaJavaJavaScriptTypeScriptC / C++
Big Data & Streaming
Apache SparkPySparkApache KafkaSpark Structured StreamingDatabricksSnowflake
Databases
PostgreSQLMongoDBElasticsearch
DevOps & Orchestration
DockerKubernetesJenkinsApache AirflowAutoSys
Observability & BI
DynatraceSplunkMicrosoft Power BI
Cloud & Principles
AWSTDDSOLIDMicroservicesEvent-Driven ArchitectureFault-Tolerant Design
Open Source

Personal Projects

🗄️
node-express-postgres

A RESTful API built with Node.js, Express, and PostgreSQL implementing a bank account transfer system. Features relational data modelling with customers, accounts, and account types — supporting transactional fund transfers with proper foreign-key constraint handling.

Node.jsExpressPostgreSQLREST APISQL
🖥️
dynamic-screen-management

A Node.js/Express project for managing dynamic screen rendering and routing logic. Structured with modular actions, routes, and OpenAPI specifications — demonstrating clean server-side architecture and component-based screen control.

Node.jsExpressJavaScriptRESTOpenAPI
More on GitHub

View all repositories and contributions

github.com/Namrathaputrev ↗
Education

Academic Background

BNMIT, Bangalore
B.E. in Computer Science & Engineering
Aug 2014 – Jun 2018
CSE
Let's Connect

Open to Opportunities

Based in Bangalore, India · Open to remote & hybrid roles

Interested in data engineering, streaming systems & platform development

namputrev15@gmail.com

+91 94813 24896