Chanakya Madasi

I'm Chanakya, a Senior Data Engineer who's spent five years turning messy data chaos into clean, governed pipelines at scale. I've built Medallion lakehouses processing 100M+ records daily, contributed to GenAI tooling using MCP, and earned my Databricks Professional certification because I genuinely love what I do.

Plain caught my attention because you're building something most companies treat as an afterthought great customer support infrastructure. I've seen firsthand how poor tooling slows down support teams, and I want to help build the data backbone that makes Plain genuinely fast, reliable, and insightful for the companies that depend on it. The intersection of engineering rigour and real customer impact is exactly where I want to be.

Senior Data Engineer at 7Dxperts UK Limited (2023-08 – Present)

• Designed and implemented batch and streaming ETL/ELT pipelines using Databricks, PySpark, and Delta Lake, processing over 50 million time-series and geospatial records daily across AWS infrastructure.
• Architected a full Medallion Lakehouse (Bronze/Silver/Gold) with governance enforced through Unity Catalog, establishing lineage tracking and role-based access control across the platform.
• Developed DLT declarative pipelines with LakeFlow orchestration, implementing Type 1 and Type 2 SCD patterns, DQ expectations for row-level validation, and config-driven ingestion enabling teams to onboard new sources without bespoke code.
• Operated as a Databricks platform engineer, configuring workspace networking, diagnosing connectivity issues, and evaluating new Data Engineering features in pre-release testing cycles.
• Integrated AI/BI Genie dashboards with curated Gold-layer Delta tables, enabling business users to query governed datasets through natural-language prompts.
• Tuned large-scale Spark workloads through partitioning, Z-ordering, broadcast joins, and caching strategies; enabled Photon-accelerated processing for high-volume jobs.
• Provisioned Databricks workspaces, cluster policies, and Unity Catalog resources using Terraform; automated deployment across Dev/Test/Prod with GitHub Actions and Asset Bundles.
• Contributed to internal GenAI tooling using MCP, automating routine tasks such as schema validation and pipeline health checks.
• Owned SDLC documentation including technical design specs, data flow diagrams, and deployment runbooks, ensuring audit readiness and smooth knowledge transfer.
• Enabled secure cross-platform data sharing via Delta Sharing and Snowflake; mentored junior engineers on Spark optimisation best practices.

Data Science Engineer at Zsah Private Limited (2021-11 – 2023-07)

• Engineered large-scale Databricks ELT pipelines on AWS and Azure, handling 100M+ records daily for financial analytics with incremental loading via CDC and Delta MERGE.
• Developed a cross-platform data quality framework compatible with Spark, SQL, Databricks, Snowflake, and BigQuery, capable of rejecting, correcting, or flagging records against fine-grained business rules.
• Built a fully automated geospatial pipeline extracting road network data from PBF planet files; created an OSRM network analysis model producing shortest routes, journey matrices, and accessibility data.
• Designed Spatial KNN and hierarchy-based clustering models for store grouping and customer location optimisation; developed Python APIs to serve GeoJSON from PostGIS.
• Converted an R-based isolines library into a Python package using Databricks SQL and Mosaic spatial functions for routing and isoline solutions.
• Supported data warehouse migration from legacy systems to AWS Redshift; gained exposure to Azure Synapse Analytics for schema conversion and performance benchmarking.
• Implemented MLflow for model tracking and lifecycle management; conducted R&D on LangChain and LLM integration, building prototypes that generated SQL from natural-language business questions.

Data Analyst (Intern) at Zsah Private Limited (2020-08 – 2021-09)

• Managed data loading of COVID-related airline updates into PostgreSQL, ensuring timely and accurate ingestion during a fast-moving reporting cycle.
• Developed reusable Python templates for metadata extraction and data profiling across files and database tables.
• Built ML models (KNN, outlier detection) to identify nulls and anomalies in datasets, applying automated corrections aligned with business rules.
• Developed CNN models to detect damaged walls from client housing image data, deployed and tracked using MLflow on Databricks.

MSc in Data Science – University of Surrey (2019 – 2021)

BEng in Information Science and Technology – Visvesvaraya Technological University (2014 – 2018)