Naman is available for hire
Hire NamanNaman Jain
Verified Expert in Engineering
Data Engineering Architect and Lead Developer
Fairfax, VA, United States
Toptal member since July 8, 2019
Bio
Naman is a highly experienced cloud and data solutions architect with over 10 years of experience delivering data engineering services to multiple Fortune 100 clients. He has successfully managed petabyte-scale data migrations and built big data infrastructures using Azure Cloud, AWS Cloud, Snowflake, and dbt. Naman has created significant efficiencies in various use cases. He fundamentally believes in over-communication, establishing trust, and taking ownership of his deliverables.
Portfolio
House Kling LLC
Data Engineering, Data Pipelines, Amazon Web Services (AWS), API Integration...
Enterprise Client
Snowflake, Data Build Tool (dbt), Spark, GitLab, Data Migration...
Enterprise Client (via Toptal)
Scala, Spark, Azure, Azure Data Factory (ADF), Azure Data Lake...
Experience
- Data Engineering - 8 years
- Data Migration - 6 years
- Apache Spark - 5 years
- Scala - 5 years
- Databricks - 3 years
- Machine Learning - 3 years
- Azure Data Lake Analytics - 2 years
- Azure Data Factory (ADF) - 2 years
Preferred Environment
Azure Cloud Services, Apache Spark, Scala, IntelliJ IDEA, Git, Linux, Snowflake, Data Build Tool (dbt), Data Migration, AWS IoT
The most amazing...
...enterprise-grade, big data ELT platform I've delivered in Azure Cloud was a single source of truth Data Lakehouse that enabled a wide diversity of use cases.
Work Experience
Senior Data Engineer – Financial Data Integration
2025 - 2025
House Kling LLC
- Designed and implemented scalable ETL pipelines to ingest and process multi-asset financial data (stocks, options, futures, and alternative instruments) from APIs and third-party vendors, handling terabyte-scale daily data volumes.
- Built a cloud-native data architecture on AWS (leveraging S3, Lambda, and orchestration services) to automate high-frequency (hourly/daily) ingestion, transformation, and storage of structured and semi-structured financial datasets.
- Engineered a terabyte-scale Snowflake data warehouse, optimizing schema design and partitioning strategies to support high-performance analytics and downstream LLM/RAG workloads.
- Integrated multiple financial data providers (e.g., Polygon.io, Alpaca, and social sentiment sources such as Twitter) via API-based ingestion frameworks, improving data coverage and timeliness for analytics use cases.
- Developed robust data validation and cleansing pipelines, implementing schema normalization and quality checks that reduced data inconsistencies and improved reliability of aggregated datasets.
Technologies: Data Engineering, Data Pipelines, Amazon Web Services (AWS), API Integration, Python, Amazon Kinesis, Apache Kafka, AWS Glue, Amazon OpenSearch, Large Language Models (LLMs), LangChain, Firebase, Data Analysis
Senior Data Analytics Engineer
2021 - 2024
Enterprise Client
- Architected and delivered the clients' entire PROD logic lift (over 200 SQL workflows) and legacy data migration (over 10 Petabytes) from AWS Redshift to Snowflake.
- Automated the daily ingestion jobs via the data build tool (dbt) and created a self-updating data catalog via dbt Cloud.
- Lifted all SQL logic from Redshift SQL to dbt SQL. Used macros and Jinja, which allowed us to get visibility into very complex SQL logic and visualize it via the catalog.
- Achieved an 80% reduction in time, cost, and real-time materialization of all our client-facing BI reports due to a cascading trigger in dbt, as opposed to using Redshift, where the sequential tables all got refreshed at once.
- Built and launched interactive dashboards in Looker to help visualize important eCommerce metrics like sales, customer behavior, and inventory management.
- Connected Looker with large-scale eCommerce data sources like SQL databases and Google BigQuery to make it easier for teams to access and report on data.
- Worked closely with stakeholders to understand their needs, turning complex data into clear, actionable insights through Looker’s dashboards.
- Improved the performance of Looker dashboards, cutting down query processing time by 30% with better data source management.
- Helped the stakeholders understand the cost vs benefit of having real-time vs near real-time reports, thereby saving the client significant $ in potential compute costs.
- Automated reporting in Looker to provide near real-time insights, helping teams track key metrics like conversion rates, customer lifetime value, and cart abandonment.
Technologies: Snowflake, Data Build Tool (dbt), Spark, GitLab, Data Migration, Data Warehouse Design, Big Data, Data Pipelines, ELT, Big Data Architecture, Data Architecture, Looker, Change Data Capture
Cloud Solutions Architect
2020 - 2021
Enterprise Client (via Toptal)
- Worked on the orchestration and automation of the workflows via Azure Data Factory.
- Optimized and partitioned storage in Azure Data Lake Storage (ADLS) Gen2.
- Implemented complex, strongly-typed Scala Spark workloads in Azure Databricks along with dependency management and Git integration.
- Implemented real-time low cost and low latency streaming workflows which at their peak were processing more than 2MM raw JSON blobs per second. Integrated Azure Blob Storage, Azure Event Hubs, and Azure Queues via ABS-AQS.
- Created a multi-layered ELT platform that consisted of raw/bronze (Azure Blob Storage), current and silver (Azure Delta Lake), and mapped/gold (Azure Delta Lake) layers.
- Balanced the cost of computing by spinning up clusters on demand versus persisting them.
- Made big data available for efficient and real-time analysis throughout the client via Delta tables, which provided indexed and optimized stores, ACID transaction guarantees, and table level and row-level access controls.
- Tied all together in end-to-end workflows that were either refreshed with just a few clicks or automated as jobs.
- Led a team of five consisting of four developers and one solutions architect to productionize big data workflows in Azure Cloud, enabling the client to sunset its legacy applications and experience far more reliable and scalable prod workflows.
- Enabled a wide diversity of use cases and future-proofed them by relying upon open source and open standards.
Technologies: Scala, Spark, Azure, Azure Data Factory (ADF), Azure Data Lake, Azure Databricks, Delta Lake, Data Engineering, ETL, Data Migration, Databricks, Big Data, Data Pipelines, ELT, Big Data Architecture, Azure Cloud Services, Azure Event Hubs, Data Architecture, Azure Data Lake Analytics, Data Lakes, Change Data Capture, Data Mining
Lead Data Engineer
2019 - 2020
Stealth mode AI startup (Series A $20 Million)
- Architected and implemented a distributed machine learning platform.
- Productionized 20+ machine learning models via Spark MLlib.
- Built products and tools to reduce time to market (TTM) for machine learning projects. Reduced the startup's TTM from the design phase to production by 50%.
- Productionalized 8 Scala Spark applications to transform the ETL layer to feed into the machine learning models downstream.
- Used Spark SQL for ETL and Spark Structured Streaming and Spark MLlib for analytics.
- Led a team of six comprising of three data scientists, two back-end engineers, and one front-end engineer. Delivered a solution that had a back-end layer that talked to the front end via REST API and launched and managed Spark jobs on demand.
Technologies: Data Engineering, Apache Hive, Apache Impala, SQL, Apache Spark, Scala, Bash, Linux, Spark Structured Streaming, Machine Learning, MLlib, Spark, Spark SQL, ETL, Big Data, Data Pipelines, ELT, Big Data Architecture, Data Architecture, Data Lakes
Senior Data Engineer
2018 - 2019
Dow Chemical (Fortune 62)
- Created five Scala Spark apps for ETL and wrote multiple Bash scripts for the automation of these jobs.
- Architected and built a Scala Spark app to validate Oracle source tables with their ingested counterparts in HDFS. The user can dynamically choose to conduct either a high-level or data-level validation.
- Developed the application so that its output would be the exact mismatched columns and rows between source and destination in case of a discrepancy.
- Reduced the engineer's manual debugging workload by over 99% by lowering it to just running the application and then reading the human-readable output file.
- Delivered the entire ETL and validation project ahead of schedule and under budget.
- Used Cloudera Distributed Hadoop (CDH) for HDFS and Hive extensively.
Technologies: Data Engineering, Apache Hive, Apache Impala, SQL, Apache Spark, Scala, Hadoop, Bash, Linux, Oracle Database, Spark SQL, ETL, Big Data, Data Pipelines, ELT, Big Data Architecture, Data Architecture
Senior Data Engineer
2018 - 2019
Boston Scientific (Fortune 319)
- Designed and implemented a Scala Spark application to build Apache Solr indices from Hive tables. The app was designed for a rollback on any failure and reduced the downtime for downstream consumers from around 3 hours to around 10 seconds.
- Implemented a Spark Structured Streaming application to ingest data from Kafka streams and upsert them into Kudu tables in a Kerberized cluster.
- Set up multiple Shell scripts to automate Spark jobs, Apache Sqoop jobs, and Impala commands.
- Used Cloudera Distributed Hadoop (CDH) and ElasticSearch extensively.
Technologies: Data Engineering, Apache Hive, Apache Impala, SQL, Apache Spark, Scala, Hadoop, Bash, Linux, Kudu, Spark Structured Streaming, Apache Solr, Spark SQL, ETL, Big Data, Data Pipelines, ELT, Big Data Architecture, Data Architecture
Senior Data Engineer
2017 - 2018
General Mills (Fortune 200)
- Consumed social marketing data from various sources, including Google Analytics API, Oracle Databases, and various streaming sources.
- Created a Scala Spark application to ingest >100Gb of data as a daily batch job, partition, and store as parquet in HDFS, with corresponding Hive partitions at the query layer. The app replaced a legacy Oracle solution and reduced runtime by 90%.
- Set up Spark SQL and Spark Structured Streaming for ETL.
- Used Cloudera Distribution Hadoop (CDH) extensively.
Technologies: Data Engineering, Apache Hive, Apache Impala, SQL, Apache Spark, Scala, Hadoop, Spark Structured Streaming, Spark SQL, ETL, Big Data, Data Pipelines, ELT, Big Data Architecture, Data Architecture
Software Engineer
2015 - 2016
MetLife Insurance (Fortune 44)
- Acted as the product manager for a motorcycle insurance web app. The app grew into becoming the primary landing site for motorcycle insurance leads.
- Built master for deployment until production. Deployed all builds and was primary on the stability of the build.
- Led Scrum development for client teams of 30+ developers, testers, and analysts.
- Architected and supported the solution within the client organization.
Technologies: Model View Controller (MVC), Agile
Experience
Optimizing Capital Allocation for Mortgage Market Loans
https://github.com/Namanj/Mortgage-Market-Tri-AnalysisThis project was developed as a 2-week capstone project for Galvanize's data science program.
I worked with data from Shubham Housing Finance, a firm that has given out more than USD $150 million as mortgage loans over the past 5 years.
My goal was to use data science to help the firm optimize its usage of capital, both in its loan allocation process and in its expansion.
I decided to break this broad goal down into 3 individual more specific goals:
- Build a classifier that predicts the probability that a customer will default on their loan
- Recommend new office locations which maximize growth potential
- Forecast upcoming amount of business over the next quarter
I worked with data from Shubham Housing Finance, a firm that has given out more than USD $150 million as mortgage loans over the past 5 years.
My goal was to use data science to help the firm optimize its usage of capital, both in its loan allocation process and in its expansion.
I decided to break this broad goal down into 3 individual more specific goals:
- Build a classifier that predicts the probability that a customer will default on their loan
- Recommend new office locations which maximize growth potential
- Forecast upcoming amount of business over the next quarter
Education
2012 - 2014
Bachelor of Science Degree in Computer Science and Engineering
The Ohio State University - Columbus, Ohio, USA
Certifications
DECEMBER 2017 - PRESENT
Spark and Hadoop Developer
Cloudera
JANUARY 2016 - PRESENT
Data Science Bootcamp
Galvanize | San Francisco, California, USA
Skills
Libraries/APIs
Spark ML, Snowpark, MLlib, Google APIs
Tools
Git, IntelliJ IDEA, Spark SQL, Apache Impala, Apache Solr, Kudu, Apache Sqoop, Subversion (SVN), GitLab, Looker, AWS Glue, Amazon OpenSearch
Languages
Scala, SQL, Snowflake, Python 3, Bash, Python
Frameworks
Spark, Apache Spark, Play Framework, Spark Structured Streaming, Hadoop, Yarn
Paradigms
ETL, ETL Implementation & Design, Functional Programming, Microservices Architecture, Object-oriented Programming (OOP), Agile Software Development, Agile, Model View Controller (MVC)
Platforms
Azure, Azure Event Hubs, Databricks, Linux, Apache Kafka, Firebase, MacOS, Oracle Database, AWS IoT, Amazon Web Services (AWS)
Storage
Data Lakes, Data Lake Design, Data Pipelines, Azure Cloud Services, Apache Hive, HDFS
Other
Azure Data Factory (ADF), Azure Data Lake, Data Engineering, Data Warehousing, Delta Lake, Data Migration, Azure Data Lake Analytics, ETL Development, Big Data, Data Architecture, Big Data Architecture, ELT, Azure Databricks, Data Warehouse Design, Data Build Tool (dbt), Change Data Capture, Machine Learning, Data Structures, Data Mining, Data Analysis, API Integration, Amazon Kinesis, Large Language Models (LLMs), LangChain
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