Oct 2021 – Feb 2025 · 3 yrs 4 mos · Bengaluru, Karnataka, India · Remote

• Following the acquisition of Slintel by 6sense, I led the data engineering efforts to migrate core data pipelines to the new infrastructure—ensuring a smooth transition with minimal downtime and full data integrity.
• Key Highlights:
• 1. Seamless Migration & Leadership: Led the end-to-end migration of data pipelines, coordinating across teams to ensure continuity, accuracy, and zero business disruption.
• 2. Documentation & Enablement: Authored detailed technical documentation and conducted training sessions to onboard engineers onto the new infrastructure, promoting adoption and maintainability.
• 3. Platform Integration: Enabled integration of Slintel’s data assets with 6sense’s AI-driven platform, enhancing the depth and accuracy of predictive analytics and go-to-market intelligence.
• 4. Scalability & Performance: Re-engineered critical data workflows to be more scalable and resource-efficient, significantly reducing processing time and improving reliability.

Nov 2020 – Sep 2021 · 10 mos · Bengaluru, Karnataka, India · Remote

• Project: Data Management for Slintel – Sales Intelligence platform
• Description: On joining the startup as 2nd data engineer, helped build robust data pipelines to process and analyze large volumes of data efficiently, ensuring high reliability and performance
• Highlights:
• COLLABORATIVE DEVELOPMENT: Developed data pipelines based on business rules, while collaborating closely with cross-functional teams, including product managers, to understand data requirements.
• DATA ANALYSIS: Analyzing vendor’s data and providing actionable insights to team which helped take decision if we really want to onboard the vendor. Along with this 95% product decisions were powered by data analytics results over billions of data points.
• DATA QUALITY: Quality Check Reports were generated at different layers of data to capture ambiguity if any during data processing.
• TOOLS AND TECHNOLOGIES: Leveraged tools and technologies such as Apache Spark, Python, Elasticsearch, MongoDB, AWS

Oct 2019 – Nov 2020 · 1 yr 1 mo

• Project: Data Management for American multinational financial services corporation.
• Description: Helped the organization, get rid of manual and time taking processes by putting up ideas and implementing those using python/pyspark. Also streamlined their Merchant Analysis and Research tool to make it robust and user friendly.
• Responsibilities:
• Guided the team and pipelined the process of enriching address information using the data received from 3rd party data sources.
• Worked on multiple PYSPARK modules for parsing the incoming data (various file formats) and creating/updating an automated file tracker.
• Improved SPARK SCALA based Merchant Analysis and Research tool by making it configuration driven, added many new features, increased efficiency.

Sep 2018 – Oct 2019 · 1 yr 1 mo

• Project: Data Management for U.S. based retail technology company
• Description: Optimized and improved the existing data architecture of the retail technology company by setting up a configuration driven environment over Google Cloud Platform (GCP) which helped achieve reusability and robustness.
• Responsibilities:
• Created retailer configuration file with all the details which would help parameterizing the python/spark code for various processes and used that to create multiple processes using common pyspark data processor file.
• Developed python code for airflow(scheduler) configuration file such that for a newly registered retailer, just adding retailer name to an existing process in configuration file would create an airflow DAG for the new retailer.
• Increased performance of the Spark codes on read/write by moving to parquet file format and defining schema and nullability of columns.
• Implemented unit testing for every piece of python and pyspark codes to have a trusted code in place.

Apr 2017 – Sep 2018 · 1 yr 5 mos · Bengaluru, Karnataka, India

• Developed Hadoop scripts based on business rules, while collaborating
• with various stakeholders to perform source-to-target data mapping,
• design and review.
• Handled processing of large sets of structured, semi-structured and
• unstructured data and supporting data systems architecture.
• Implemented Partitioning, Dynamic Partitioning, and Bucketing in Hive
• using internal and external tables.
• Optimized existing code in Hadoop using SparkSQL and
• Data Frames.
• Worked on migrating legacy Map Reduce programs into Spark
• transformations using PySpark.

Oct 2016 – Mar 2017 · 5 mos · Bengaluru, Karnataka, India

• Facilitated insightful daily analysis of 40 to 50GB of customers data
• stored in the data warehouse.
• Created pig scripts to spot any data discrepancy by comparing
• fresh data with historic data in repository.
• Automated alerts to the team through Oozie in case of discrepancies in
• customer's data.
• Handled data movement from UNIX file system to HDFS and vice versa
• and processed data using shell scripting.