CHAKRADHARMUDDASANI
Professional Summary
Data Engineer with 4+ years of experience building production data pipelines, analytics platforms, and forecasting models for operations and enterprise analytics teams. Skilled in Python and SQL and experienced designing Snowflake-based data warehouses and AWS data ingestion using S3, Redshift, Glue and Athena. Delivered end-to-end ETL/ELT pipelines with automated data quality, schema design, and CI/CD deployments using Jenkins and Azure DevOps to improve data reliability for reporting. Built interactive dashboards with Tableau and Power BI and operationalized Scikit-Learn models for anomaly detection and time-series forecasting to support capacity planning. Worked across consulting and product engineering environments, converting large, complex datasets into analysis-ready tables and self-serve BI assets. Proven ownership of data architecture, cross-functional requirements, and mentoring junior engineers while documenting standards for reproducible analytics and performance.
Technical Skills
Work Experience
- Built automated ETL pipelines in AWS Glue to ingest and transform operational data into Snowflake, enforcing documented schemas and automated quality checks.
- Modeled unified reporting datasets in Amazon Redshift using SQL to power downstream analytics and reporting.
- Implemented CI/CD deployments for data pipeline releases using Jenkins and Git to standardize deployments and reduce release variability.
- Developed time-series forecasting and capacity planning models in Python to support operational resource procurement and planning.
- Diagnosed data anomalies and surfaced optimization opportunities by combining Snowflake SQL and Scikit-Learn-based detection routines.
- Mentored junior engineers and authored data handling standards and schema design documentation to improve team onboarding and reproducibility.
- Analyzed large operational datasets with Python and Pandas to uncover trends and correlations for high-impact client projects.
- Developed forecasting and predictive models with Scikit-Learn to support planning and budgeting decisions.
- Designed and managed end-to-end data pipelines in Snowflake to handle ingestion, transformation, validation, and quality monitoring.
- Built interactive Tableau dashboards to enable real-time KPI monitoring for executive and engineering stakeholders.
- Automated data collection and reporting using Python and SQL to improve timeliness of decision-ready datasets.
- Presented analytical findings and recommendations to technical teams and non-technical leadership to drive data-informed actions.
- Applied Scikit-Learn to develop models that extracted actionable business insights and integrated results into analytics workflows.
- Implemented anomaly-detection processes using Scikit-Learn that reduced cross-system data discrepancies by 30%.
- Developed complex SQL queries and stored procedures in Snowflake to support large-scale data extraction and transformation.
- Designed interactive Tableau and Power BI dashboards and automated KPI reporting workflows, saving approximately 5 hours/week of manual effort.
- Automated data validation scripts in Python to provide analysis-ready datasets to stakeholders.
- Communicated analytical findings and operational recommendations to technical and non-technical stakeholders.
- Developed Tableau and SSRS dashboards to provide real-time operational visibility for client stakeholders.
- Implemented Scikit-Learn models for anomaly detection and pattern analysis on operational datasets.
- Built and optimized ETL pipelines using SQL Server stored procedures and T-SQL to improve reporting accuracy.
- Automated reporting solutions across MySQL to reduce manual intervention in recurring reports.
- Managed CI/CD deployments for data deliverables using Azure DevOps to streamline release processes.
- Documented data processes and contributed within Agile/Scrum delivery cycles to maintain delivery quality.
- Analyzed data trends using SQL to support business decision-making processes.
- Authored SQL scripts for data extraction, cleaning, and validation to produce consistent datasets.
- Generated QA documentation and analytical reports to verify accuracy of stakeholder deliverables.
- Created visual reports using data visualization tools to communicate findings to team members.
- Validated datasets and collaborated with team members to prepare analysis-ready data.
- Streamlined data intake by creating standardized validation checks and templates to improve data consistency.
Education
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