Jun 2023 – May 2025 · 1 yr 11 mos

• First hire in the data team.
• Designed and launched Relcu’s data monetization strategy (pricing, packaging, customer validation) in close partnership with the CEO, CPO, CDO, converting data from a support function into a recurring revenue stream of ~$5K per client per month.
• Scaled Relcu’s Data & ML platform from 0 → 30+ paying customers, owning platform reliability, performance, and cost efficiency while strengthening product market fit in the financial services CRM space.
• Built and led a high ownership Data team, establishing delivery standards, architectural decision clarity, and coaching practices that supported rapid platform scaling.
• Introduced AI / ML powered conversational intelligence (transcription, summarization, diarization, sentiment analysis, keyword extraction) and multi channel engagement scoring across calls, text, and email, saving clients ~3,000 hours of manual work per month and improving sales effectiveness.
• Engineered data ingestion and export frameworks integrating 10+ third-party sources (including Datadog and Twilio), enabling seamless data consumption for clients and empowering internal teams with richer operational and customer insights.
• Worked closely with product and design to surface ML and data insights directly in the application UI, and built no code, data generation, and masking tools that accelerated client onboarding, reduced engineering dependency, and improved testing at scale.
• Owned monthly cost tracking, budgeting, and optimization for Data & ML infrastructure, ensuring efficient resource usage as the platform scaled in customers and data volume.

Nov 2021 – Jun 2023 · 1 yr 7 mos

• Worked with UK and South East Asia Clients providing premium tech consultancy.
• Advised global enterprise clients across the UK, Japan, Singapore, and Germany, translating complex legacy constraints into practical data platform, governance, and cloud modernization decisions.
• Worked on modernization of a data mesh platform on AWS for an 80 year old enterprise, processing 2000+ TB of data and supporting 10 teams across 3 continents, enabling faster, evidence based marketing decision making through automated quality checks, cataloging, and governance.
• Designed and deployed a cloud native data platform on Azure using Databricks, Data Factory, and Power BI, while architecting end to end infrastructure with Terraform (CI/CD, high availability, monitoring, release management, migrations), improving analytics reliability, performance, and operational efficiency.
• Implemented full-stack observability using OpenTelemetry, enabling centralized logging, metrics, and distributed tracing to improve production visibility and incident diagnosis.
• Mentored interns and early-career engineers and led workshops on data privacy, data mesh, and governance, strengthening engineering practices across teams.

Jul 2020 – Nov 2021 · 1 yr 4 mos

• Second hire in the data team.
• Built and scaled a near real time events pipeline handling 100M+ events per day with 99.9% data accuracy, powering user funnels and analytics in near real time while meeting strict SLOs on an open source stack.
• Designed and scaled batch data pipelines supporting 2,000+ entities and vendors, enabling reliable metrics, dashboards, and reporting across business and operations teams.
• Engineered a SQL query engine framework over the data lake, enabling flexible, performant analytical querying for data analysts and data scientists.
• Built a comprehensive data validation framework and implemented a schema registry to enforce event contracts, manage schema evolution, and significantly reduce data quality regressions in production.
• Improved data discoverability and literacy by launching a data discovery platform using Amundsen, increasing visibility and adoption of data assets across the organization.
• Partnered with data scientists to develop PySpark based ML pipelines, providing scalable data access patterns and infrastructure support for multiple ML use cases.
• Built end to end observability and deployment automation for data and ML platforms (alerting, monitoring, logging, CI/CD), improving operational reliability and reducing manual intervention during releases.

Apr 2019 – Jun 2020 · 1 yr 2 mos

• First hire in the data platform team.
• Built Urban Company’s Customer Data Platform from scratch, ingesting transactional, clickstream, and server events to support real time and batch analytics for data, product, business, operations, sales, and marketing teams.
• Designed and launched the company’s first Master Data Management platform, aggregating data from multiple vendors and internal systems with an intuitive onboarding UI, establishing a single source of truth across the organization.
• Developed event ingestion microservices integrating sources such as Appsflyer and Google Ads, enabling reliable attribution, funnel analysis, and growth analytics at scale.
• Owned and optimized a 200+ TB data warehouse, improving query performance, reliability, and cost efficiency as data volume and usage grew.
• Led major platform modernization initiatives, migrating the data platform from Python → Spark, the data warehouse from Redshift → Snowflake, and BI tooling from Tableau → Looker, significantly improving scalability, performance, and analyst productivity.

Jan 2017 – Mar 2019 · 2 yrs 2 mos

• Early hire in the data product team.
• Early hire in the data product team, building resilient, large scale healthcare analytics systems for US healthcare clients using HBase, HDFS, Spark, and Scala, supporting mission critical, data driven products.
• Developed and scaled Patient 360, integrating patient data from multiple heterogeneous clinical and operational sources into a unified patient view used across analytics and product workflows.
• Designed and optimized a rule based record linkage and deduplication framework, enabling accurate patient identity resolution and providing a single enterprise wide identifier for downstream systems.
• Built RESTful APIs using Scala Play Framework to verify, create, and manage patient records, streamlining data access and improving reliability of patient data ingestion workflows.
• Implemented a custom Spark job executor to schedule and manage batch processing workloads, improving processing reliability and operational control for large scale healthcare datasets.