Nayanika Mula
@nayanikamula
I build production AI-agent workflows and data systems with verified outputs, governed access, and reliable pipelines.
IndiaWhat I'm looking for
I'm building production AI-agent workflows at Datalogz, where I use Claude-based coding agents to ship Python systems with reviewable, reversible changes. I designed a call-summary agent and independent verifier that blocks unsupported claims before they reach the CRM.
I've built MCP servers for governed internal-data access, reusable Claude skills, and a content pipeline that generates FAQ and JSON-LD schema from Ghost CMS posts with human review. I also engineered reverse ETL pipelines that refreshed about 7,500 CRM records with zero data incidents.
Before that, I re-architected multi-tenant metadata pipelines for more than nine Fortune 2000 enterprise pilots, improving data-model reusability by about 60% across dbt, Snowflake, and PostgreSQL.
At Capital One, I migrated 25+ high-throughput financial pipelines to distributed Spark, reducing batch processing from eight hours to under two. I also built ETL pipelines processing 500GB+ of raw data daily at 99.8% accuracy.
Experience
Work history, roles, and key accomplishments
GTM / Growth Data Engineer
Datalogz
Jan 2026 - Present (7 months)
Planned, implemented, debugged, and refactored roughly 10,800 lines of production Python across a dozen systems in daily collaboration with AI coding agents, with idempotent writes, dry-run modes, and per-run audit logs. Designed a call-summary agent paired with an independent verifier agent to classify generated claims before CRM writes, and built an MCP server for governed data access.
Senior Data Engineer
Datalogz
Aug 2025 - Jan 2026 (5 months)
Re-architected metadata pipelines for a multi-tenant production platform, supporting 9+ parallel Fortune 2000 enterprise pilots through modular, tenant-isolated dbt models. Redesigned dbt and Snowflake/PostgreSQL data models to improve reusability by about 60% while maintaining governance, observability, and lineage guardrails.
Built clean, reusable, context-rich data products to prepare enterprise data for AI and LLM consumption. Migrated 25+ high-throughput financial data pipelines from legacy systems to distributed Spark jobs, cutting batch processing from 8 hours to under 2.
Built ETL pipelines processing 500GB+ of raw data daily at 99.8% accuracy, with performance monitoring achieving 99.9% uptime and 15% lower latency across critical pipelines.
Education
Degrees, certifications, and relevant coursework
University of Texas at Arlington
Master of Science, Business Analytics
2017 - 2019
Pursued a Master of Science in Business Analytics, focusing on data-driven decision making and analytical skills.
NIIT University
Bachelor of Technology, Computer Science
2013 - 2017
Earned a Bachelor of Technology in Computer Science, building a strong foundation in software engineering and computer science principles.
Tech stack
Software and tools used professionally
Availability
Location
IndiaAuthorized to work in
Job categories
Skills
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