Jignesh Bejjagam

* Designed and maintained scalable data pipelines to ingest data from POS systems, e-commerce platforms, and third-party APIs using SQL and Python * Built and optimized ETL/ELT workflows to transform raw data into structured, analysis-ready datasets with a focus on data quality and reliability * Developed and managed data models (star/snowflake schemas) to support efficient querying and business intelligence reporting * Implemented data warehousing solutions to centralize and standardize retail data, improving accessibility for analytics teams * Created interactive dashboards and reports using Power BI/Tableau to monitor KPIs such as sales trends, inventory turnover, customer segmentation, and revenue growth * Performed exploratory data analysis (EDA) and statistical analysis to identify patterns, anomalies, and business opportunities * Collaborated with cross-functional teams (marketing, operations, finance) to translate business requirements into technical data solutions * Automated recurring reporting processes, reducing manual effort and improving reporting efficiency * Monitored and optimized query performance, reducing execution time and improving system scalability * Implemented data validation, cleansing, and governance practices to ensure data accuracy and consistency * Supported real-time and batch data processing use cases to enable timely decision-making * Documented data pipelines, schemas, and workflows to maintain transparency and support knowledge sharing across teams

• Designed and maintained scalable ETL/ELT pipelines ingesting data from ServiceNow REST APIs, external systems, and event-based sources into Snowflake, Azure Data Lake, and Databricks. • Built Python-based data transformation frameworks (pandas, JSON/XL parsing, APs) to clean, normalize, validate, and enrich datasets for analytics and Al workflows. • Developed and evolved enterprise data models (dimensional, relational, Delta Lake) supporting Bi, predictive analytics, and GenAl use cases. • Implemented Spark-based pipelines in Databricks, optimizing Delta Lake storage, incremental loads, and job performance for large-scale public-sector datasets. • Orchestrated pipelines using Azure Data Factory, Apache Airflow, and Snowflake Tasks, ensuring high availability, fault tolerance, and timely delivery. • Applied data governance, security, and compliance controls, including access management, lineage, masking, and quality checks aligned with FOIP, GDPR, and Canadian standards. • Monitored and optimized production pipelines, proactively resolving failures and performance bottlenecks. • Worked closely with analytics, data science, product, and engineering teams to deliver business-aligned data solutions and contributed to the data engineering roadmap.

• Designed, built, and optimized scalable ETL/ELT pipelines using Azure Data Factory, Azure Data Lake Storage, Databricks, Snowflake, and Synapse Analytics to process large volumes of structured and semi-structured data. • Developed high-performance Spark applications in Azure Databricks (PySpark/SQL) and implemented Delta Lake architecture to support modern, cloud-native data platforms. • Architected enterprise data warehouse solutions using dimensional modeling (star/snowflake schemas), fact/dimension design, and schema evolution for analytics and reporting. • Built orchestration and automation frameworks using Azure Data Factory, Apache Airflow, and Python to ensure reliable, fault-tolerant, and scalable data workflows. • Implemented streaming and event-driven pipelines using Kafka and messaging technologies for near real-time ingestion and operational analytics. • Performed advanced SQL development and performance tuning across MPP systems like Snowflake and Synapse, optimizing complex queries and improving throughput. • Established data quality checks, validation frameworks, and monitoring mechanisms to ensure data accuracy, reliability, and governance compliance. • Developed interactive Power BI dashboards by connecting to Azure Databricks (Delta Lake) and Snowflake using DirectQuery and Import modes. • Built optimized Power BI semantic models with advanced DAX, row-level security (RLS), incremental refresh, and KPI-driven visualizations for executive and business reporting.

Being an Engineering Enthusiast I always strive to grow my learning personally and professionally throughout my career.