Kiran Maddi

• Tech: Databricks, PySpark, AWS (S3), Azure (ADLS), SQL Server, Delta Lake, Python, Power BI, SSAS, SSRS
• Modernised legacy SSIS-based transformation logic, migrating it to a high-performance, modular PySpark framework on Databricks, significantly improving maintainability, testability, and deployment speed.
• Architected a modular ETL framework by designing a reusable Python library with clear separation of concerns, including configuration management, state handling, and domain-specific transformations.
• Engineered an incremental processing engine using Delta Lake, leveraging watermarking and Change Data Feed (CDF) to support efficient Trigger Once streaming patterns and scalable MERGE operations.
• Developed complex data deduplication logic using Spark Window functions to correctly handle out-of-order events and cross-event priority conflicts, ensuring 100% data integrity across critical Pages and Claims datasets.
• Implemented performance and security optimisations, including Spark Adaptive Query Execution (AQE) and secure ADLS/S3 authentication via custom token providers, reducing infrastructure overhead and improving runtime efficiency.
• Implemented feature flagging mechanisms to enable gradual rollout of new data pipelines and platform changes, supporting controlled releases, A/B-style validation, and rapid rollback to minimise risk in production environments.
• Built and supported a data warehouse on AWS, enabling scalable analytics over structured and semi-structured data sources.
• Implemented data quality checks and validation frameworks, increasing trust and reliability of production datasets.
• Maintained semantic models, SSAS cubes, and SSRS reports, ensuring consistency of business metrics.
• Contributed to data lineage and technical documentation, improving platform transparency and auditability.
• Mentored junior engineers and participated in on-call rotations, supporting high availability for global users.

Apr 2022 – Dec 2023 Tech: AWS (Glue, Step Functions, Redshift), Databricks, Python (PyTest), PostgreSQL, Terraform

• Architected and owned a scalable AWS Redshift data warehouse and Lakehouse environment, supporting enterprise-wide analytics and high-concurrency reporting.
• Led the strategic migration of legacy ETL workloads from AWS Data Pipelines to a modern, serverless orchestration framework using AWS Step Functions and AWS Glue, improving reliability and reducing maintenance overhead by 30%.
• Designed and implemented an automated testing framework on Databricks using Python and PyTest. This framework integrated into the CI/CD pipeline to validate data transformations, schema integrity, and business logic, significantly reducing production "silent" failures.
• Developed end-to-end ELT pipelines using AWS Glue for distributed data processing, ensuring seamless integration between S3 data lakes and Redshift clusters.
• Implemented automated data quality monitoring and alerting frameworks within Step Functions workflows, ensuring high data trust for executive-level Power BI dashboards.
• Provided technical leadership and rigorous code reviews for the engineering team, enforcing standards for Terraform (IaC) and modular PySpark code to ensure delivery consistency.

• Tech: AWS, Redshift, Python, PostgreSQL, Terraform, Power BI
• Designed and delivered enterprise-scale dimensional models on Redshift for analytics and reporting.
• Built automated ETL workflows using Python, shell scripts, and cron, ensuring reliable ingestion.
• Troubleshot complex production issues via Linux/SSH, analysing logs and resolving failures proactively.
• Developed advanced SQL using CTEs, window functions, and performance tuning techniques.
• Implemented a Terraform-based FTP automation framework, enabling secure partner integrations.

• Tech: SQL Server, SSIS, SSAS, Python, SAS, Power BI
• Developed scalable ETL pipelines using SSIS and T-SQL to support pricing and BI analytics.
• Led migration of legacy SAS workflows to modern SQL/Python pipelines, improving performance and maintainability.
• Designed Kimball-style dimensional models for cross-functional reporting.
• Delivered dashboards and reports using Power BI, supporting commercial decision-making.

• Built advanced SQL procedures, views, and indexing strategies for pricing and modelling systems.
• Tuned SSIS packages and execution plans to improve reliability and runtime.
• Produced SAS-based reporting packs to monitor model performance.

• Developed automated reports using SAS PROC REPORT and ODS (HTML, Excel, PDF).
• Applied statistical procedures for operational and business reporting.

• Built predictive risk models using regression and survival analysis.
• Created reusable SAS macros and optimised SQL for large-scale datasets.
• Migrated legacy SAS codebases to WPS-compliant versions.

• Developed SAS programs for financial reporting and data transformation.
• Streamlined extraction and reporting processes.