Rishikesh Srinivas

Software Development Engineer II / Data Engineer – Team Lead & Solutions Architect
(Promoted from SDE I – Sept 2024)

DataOps & Platform Architecture

• Designed a multi-tenant AWS S3 + Apache Iceberg data lake with Trino connector for high-performance SQL analytics across 9 + tenants.
• Deployed Apache Airflow with S3-synced DAGs and a Spark cluster in client mode, enabling auto-scaling slave nodes and reducing operational costs.
• Implemented a schema-evolution framework with versioned models and dbt testing, ensuring seamless upstream changes and non-breaking pipelines.
• Built a reusable Python alerting middle layer providing on-call and custom notifications for all data tools and pipelines, centralising monitoring and improving incident response.
• Implemented Citus-sharded PostgreSQL with per-shard replicas and automated fail-over, distributing load and improving query performance by ~40 %.
• Engineered data-encryption pipelines for S3 and data-warehouse loads, ensuring full PCI DSS and SOC audit compliance.

Data Engineering & Reporting

• Built the initial Python/Pandas ETL pipeline; integrated Airbyte ingestion and dbt transformations for scalable, maintainable data flows.
• Developed a multi-threaded Python reporting stack for transaction monitoring, reducing report generation time by 60 % and increasing compliance-team productivity by 30 %.
• Created custom data connectors and optimised SQL queries, improving dashboard refresh speed by 50 %.

Application Engineering & Architecture

• PDF Generation Service: Co-architected and coded a Java Spring Boot microservice capable of generating 5 million PDFs/day, with dynamic templates, encryption, and secure delivery.
• Reporting Portal: Designed and maintained a multi-tenant reporting portal that allows users to configure reports and deliver them to email, SFTP, S3, or other blob stores.
  Supported connectors for Trino, MySQL, and PostgreSQL, enabling simultaneous multi-database report generation without performance degradation.
  Included advanced features such as encryption, hashing, and role-based security controls, meeting stringent financial-data requirements.
• Designed service APIs, caching strategy, and concurrency controls to maintain sub-second response times during peak loads.
• Implemented CI/CD pipelines and Kubernetes deployments ensuring 99.99 % uptime.

Leadership & Collaboration

• Led a 4-member cross-functional team (1 Data Modelling/Superset engineer, 2 Application engineers, plus self).
• Provided indirect mentorship to an SDE II across Data Engineering and Application Engineering, guiding architecture and code reviews.
• Partnered with product managers and compliance leads to prioritise roadmap items and establish coding/monitoring standards.

Machine Learning & Anomaly Detection (Earlier Work)

• Built sentiment-analysis and contactability-score models (F1 0.90), improving customer-contact success rate by 25 %.
• Developed an anomaly-detection model for financial transactions with 98 % fraud-detection accuracy.

• Developed a multilingual WhatsApp loan-collections chatbot using Dialogflow, cutting support response time by 30 % and increasing resolution rate by 25 %.
• Integrated Rocket.Chat for live-agent handoff and created 10 + Superset dashboards, reducing data-retrieval time by 40 %.