Mallikarjun Hatti

I was responsible for designing, developing, and implementing enterprise-level solutions to improve data accuracy, consistency, and usability across business domains. My role involved building ETL pipelines, managing master data, and applying data governance practices to ensure high-quality data delivery.

Data Insights Platform (DIP) – Data Quality Optimization.

Key Responsibilities:

• Designed and implemented a tailored data quality platform using Talend for industry-specific needs.
• Defined and enforced data governance policies, ensuring compliance with standards.
• Maintained a business glossary for improved organizational clarity.
• Standardized data elements and improved data consistency across multiple systems.
• Monitored data workflows, handled null values and duplicates, and ensured error-free integration.

Cancer Care Analytics – Predictive Modeling for Treatment Outcomes.

Key Responsibilities:

• Analyzed cancer patient datasets to identify predictive treatment outcome factors.
• Implemented data management strategies and metadata management in Qlik Sense.
• Built ETL pipelines for cleaning, transforming, and loading healthcare datasets.
• Developed predictive models in Qlik Sense to optimize treatment accuracy.
• Integrated diverse sources into Qlik Sense using data integration processes.

Telco Analytics – Customer Tenure Prediction.

Key Responsibilities:

• Designed and developed the Telco App in Qlik Sense, tailored to telecom needs.
• Applied advanced analytics to evaluate predictive models for customer churn and tenure.
• Used statistical modeling and ML techniques to build predictive models.
• Conducted performance assessments to refine and improve model accuracy.
• Delivered actionable insights for customer retention strategies.

Talend MDM Data Governance, and Workflow Automation.

Key Responsibilities:

• Designed and implemented data quality workflows in Talend MDM to manage records from multiple CSV and JSON sources.
• Resolved complex workflow errors, including datatype mismatches (Integer vs. Boolean), JSONB casting issues, and schema alignment between Talend and PostgreSQL.
• Built and optimized survivorship rules (including GID-based merge and deduplication) to maintain a single source of truth across datasets.
• Automated iteration-based file processing using tFlowToIterate, tIterateToFlow, and advanced Talend components for scalable ingestion.
• Developed PostgreSQL staging tables (e.g., product_dqstaging under the automo_dq_stage schema) for structured data integration and validation.