Laya Tigulla

Optimized data load processes, reducing load time by 40% and enhancing SLA compliance, which accelerated revenue recognition from partner analytics reports.

Led the strategic adoption of dbt Core for modular data transformations in Databricks SQL, improving collaboration and enabling version-controlled, testable, and reusable data models.

Orchestrated robust CI/CD pipelines using Jenkins and Azure DevOps, increasing deployment velocity by 60% and significantly reducing production downtime risks.

Strengthened data governance by enhancing GDPR and CCPA compliance through role-based access controls, mitigating enterprise regulatory risks and audit penalties.

Collaborated with BI teams to optimize Power BI semantic models, decreasing report latency by 35% and improving stakeholder data adoption.

Engineered and deployed near real-time streaming pipelines on Dataflow and Pub/Sub, increasing fraud prevention effectiveness by 25%.

Automated critical data workflows using Cloud Composer, reducing manual intervention time by 60% and enabling data teams to prioritize revenue-driving initiatives.

Orchestrated the migration of legacy data to Hadoop on Cloud Dataproc, enabling scalable data science workloads and unlocking historical data insights for executive dashboards.

Integrated diverse external data sources into the enterprise data warehouse (Snowflake) via secure REST/SOAP APIs, enabling advanced analytics and comprehensive reporting.

Optimized BigQuery performance for 1B+ row datasets, reducing query time by 45% and accelerating time-to-insight for product strategy leaders while maintaining robust data security.

Engineered high-accuracy ETL pipelines using PySpark and HiveSQL, powering multi-TB analytics and enabling data-driven product lifecycle decisions at scale.

Migrated on-prem data pipelines to AWS using Terraform, decreasing provisioning delays by 50% and increasing engineering velocity for R&D analytics.

Introduced Docker/Kubernetes for containerization, reducing infrastructure costs by 25% and improving environment consistency across development and production.

Achieved 99.95% system uptime through proactive monitoring with CloudWatch and Nagios, ensuring continuous data availability for global business units.

Streamlined data preprocessing using Pandas/NumPy, accelerating AI model training timelines and supporting faster go-to-market cycles.