Pinki Agarwal

• Part of the Data Engineering and Support team, managing large-scale ETL pipelines and data integration projects for SGWS’s centralised data platform.
• Developed and maintained complex ETL jobs using Informatica PowerCenter and PowerExchange to ingest data from Sapphire, SAP, mainframe systems, and third-party vendors.
• Built robust data pipelines for ingesting and transforming high-volume data into AWS Redshift, enabling downstream reporting and analytics.
• Collaborated with business users and QA teams to validate data and maintain high standards of data quality and governance.
• Led incident analysis and root cause resolution, documenting standard operating procedures and driving long-term fixes.
• Streamlined flow of information by introducing efficient ETL processes. Crafted custom SQL queries to pull relevant subsets of data for specific projects.
• Created interactive dashboards using BI tools such as Tableau, empowering non-technical stakeholders with actionable insights.
• Managed end-to-end data pipeline construction for seamless integration of disparate data sources.
• Identified opportunities for process improvement through thorough review of existing systems and procedures.
• Extracted data and distilled into usable formats for data warehousing. Maintained accurate project documentation, ensuring regulatory compliance.
• Undertook root cause analysis, leading to better problem solving.
• Designed and deployed data pipelines for integrating POS and shipment data into AWS Redshift, supporting self-service reporting and KPI dashboards for regional managers.

PROJECT HIGHLIGHTS

• Optimized ETL jobs handling logistics and warehouse data, reducing load time by 35% and improving reliability of downstream inventory reports.
• Built automated reconciliation logic to compare invoices and downstream systems using SQL and Informatica DQ, reducing manual audits and error rates.
• Supported real-time data feeds for executive Power BI dashboards, surfacing high-level insights into sales performance, territory coverage, and product demand.

• Developed and deployed IDQ mappings, mapplets, and workflows to improve data accuracy across retail master data sets.
• Designed and implemented data profiling and rule-based validation strategies for Sales , inventory, and loyalty program data.
• Created reusable Data Quality rules, scorecards, and dashboards to monitor and report on data.
• Automated address standardisation and customer deduplication using IDQ in combination with address doctor.
• Collaborated with Data Stewards, Business Analysts, and Retail Operations to define and document data quality metrics and thresholds.
• Used PowerCenter, IDQ Developer, Analyst Tool, SQL, Excel, SAP HANA and JIRA to deliver agile DQ solutions.
• Worked in Agile/Scrum environments, delivering DQ checks in sprints aligned with larger data governance initiatives.
• Improved data quality by implementing structured validation checks.

• Monitored and supported 1000+ daily ETL jobs in production using Informatica PowerCenter and Tivoli Workload Scheduler.
• Handled job failures, performance bottlenecks, and data load issues within tight SLAs.
• Performed first-level troubleshooting on failed sessions using Informatica logs and UNIX commands.
• Created and maintained UNIX shell scripts for log archiving, automated alerts, and job status reports.
• Executed and optimized SQL queries for data validation, issue diagnosis, and backend fixes.
• Coordinated with development teams to escalate critical job failures and track long-term fixes.
• Participated in 24x7 rotational on-call support, ensuring high availability of critical jobs.Provided end-to-end support for retail and finance batch processes, ensuring seamless overnight data loads.
• Assisted in job scheduling and dependency setup using TWS.
• Maintained documentation for shift handovers, issue trackers, and known error databases (KEDB).
• Suggested preventive automation scripts to reduce manual interventions in recurring batch issues.

PROJECT HIGHLIGHTS :

Informatica Batch Stabilization Initiative

• Reduced job failure rate by 30% by identifying long-running workflows and optimizing session parameters.

TWS Job Optimization

• Streamlined job dependencies and implemented alerting scripts, improving job throughput and reducing monitoring load.