Nikhil

Data Engineer with 4+ years of experience designing, developing, and optimizing enterprise-scale data platforms using Azure, AWS, Snowflake, Databricks, Apache Spark, Python, SQL, Azure Data Factory, and AWS Glue. Experienced in building scalable ETL/ELT pipelines supporting Financial Services, Healthcare, and Higher Education domains. Strong expertise in cloud data migration, data warehousing, data lake architectures, real-time and batch processing, orchestration, CI/CD, and performance optimization.

Hands-on experience with Azure Data Lake Gen2, Delta Lake, Databricks, Snowflake, Spark SQL, Power BI, and enterprise data governance. Proven ability to collaborate with business analysts, architects, data scientists, and product owners to deliver high-quality data solutions in Agile environments.

Data Engineer at Principal Financial (2024-03 – Present)

• Designed and developed scalable ETL/ELT pipelines using Azure Data Factory (ADF), Azure Databricks, and PySpark to ingest structured and semi-structured data from enterprise systems into Azure Data Lake Storage Gen2.
• Built and optimized data models in Snowflake using dimensional modeling techniques (Star/Snowflake schemas), improving reporting performance and query efficiency for financial analytics.
• Developed high-performance PySpark notebooks to transform large-scale datasets, reducing batch processing time by over 40%.
• Automated end-to-end data workflows using ADF pipelines, Azure DevOps CI/CD, and Git, ensuring reliable and repeatable deployments across Development, QA, and Production environments.
• Implemented Delta Lake architecture with ACID transactions, partitioning, and data versioning to improve data reliability and optimize analytics workloads.
• Wrote complex SQL queries, stored procedures, views, and performance tuning strategies to support enterprise reporting and business intelligence initiatives.
• Integrated data from multiple enterprise sources including SQL Server, Oracle, REST APIs, Salesforce, and flat files, ensuring high-quality, standardized data for downstream applications.
• Collaborated with business analysts, data architects, and finance stakeholders to design scalable data solutions supporting investment, risk management, compliance, and customer reporting.
• Implemented comprehensive data quality validation, monitoring, and error-handling frameworks using Azure Monitor, Log Analytics, and custom validation scripts, improving pipeline reliability.
• Ensured data governance, security, and compliance by implementing Azure Key Vault, Role-Based Access Control (RBAC), encryption, and secure access policies, while maintaining detailed technical documentation and operational runbooks.

Research Assistant (Data Analyst) at Saint Louis University (2023-09 – 2024-02)

• Collected, cleaned, and analyzed large academic and research datasets using Python (Pandas, NumPy) and SQL, improving data accuracy and supporting evidence-based research initiatives.
• Developed interactive dashboards and reports in Power BI to visualize research findings, KPIs, and statistical trends for faculty members and project stakeholders.
• Designed and optimized SQL queries, views, and stored procedures to extract, transform, and validate data from relational databases.
• Performed exploratory data analysis (EDA), statistical analysis, and trend identification using Python to generate actionable insights for ongoing research projects.
• Built automated ETL workflows to integrate data from multiple sources, reducing manual data preparation efforts and improving reporting efficiency.
• Validated data quality through cleansing, deduplication, missing value treatment, and consistency checks to ensure reliable analytical outcomes.
• Collaborated with professors, researchers, and cross-functional teams to translate business and research requirements into scalable analytical solutions.
• Created reusable Python scripts and SQL automation processes for recurring data extraction, transformation, and reporting activities.
• Documented research methodologies, data dictionaries, validation procedures, and technical specifications to support project reproducibility and knowledge sharing.
• Assisted in presenting analytical findings through visual reports, presentations, and dashboards, enabling data-driven decision-making across academic research initiatives.

Data Engineer at Quadrant Technologies (2022-09 – 2023-07)

• Designed, developed, and maintained scalable ETL/ELT pipelines using Python, SQL, PySpark, and Azure Data Factory (ADF) to integrate data from multiple enterprise systems.
• Built high-performance Apache Spark and Azure Databricks jobs to process large-scale structured and semi-structured datasets, reducing data processing time by 40%.
• Developed and optimized Snowflake data warehouse objects, including tables, views, stored procedures, and materialized views for enterprise reporting.
• Implemented Change Data Capture (CDC) and incremental loading strategies to efficiently synchronize data from SQL Server, Oracle, REST APIs, and other data sources.