RAJA NANDINI BHAVANAM
Data Engineer
Jersey City
Summary
Data Engineer with 5+ years of experience building ETL/ELT pipelines that turn data from databases, recurring files, APIs, and cloud platforms into reliable datasets for reporting and analytics. Strong hands-on experience with SQL, Python, PySpark, schema validation, de-duplication, incremental loading, historical versioning, and data-quality controls.
Write complex SQL using joins, CTEs, window functions, stored procedures, and MERGE/upsert logic to apply business rules and prepare data for downstream BI applications. Experienced in third-party API integration, production pipeline troubleshooting, and working with analysts and business stakeholders to translate reporting needs into practical, maintainable data solutions.
Overview
1
1
Certification
5
5
years of professional experience
Work History
Data Engineer
PamTen
06.2025 - Current
- Delivered an AWS-to-Snowflake migration proof of concept using AWS AppFlow and PySpark-based AWS Glue jobs, reducing transformation overhead by approximately 30%.
- Reworked SQL and PySpark ETL pipelines that cleaned and standardized data from multiple source systems, improving processing times across recurring production workloads.
- Developed SQL transformation logic using joins, CTEs, window functions, and reconciliation queries to prepare consistent datasets for analytics and reporting.
- Built reusable AWS CDK components for AWS Glue, Lambda, Step Functions, Amazon S3, and IAM, reducing repeated setup work when teams introduced new data workflows.
- Designed federated querying patterns across DynamoDB, MySQL, Athena, and Redshift Spectrum, allowing analysts to access data from multiple systems through a consistent analytical layer.
- Created curated SQL views and validated reporting datasets that fed stakeholder-facing Power BI dashboards.
- Worked with analysts to define report-ready schemas, verify business measures, and investigate differences between source-system and dashboard totals.
- Added retry handling, failure notifications, and recovery steps to AWS Glue and Step Functions workflows, reducing manual intervention during pipeline failures.
Data Engineer
HCA
09.2023 - 05.2025
- Built and maintained 6 ETL pipelines using Python and SQL to extract, clean, and standardize claims, patient, provider, and operational data from different source systems into a unified, reportable dataset.
- Automated daily incremental data loads, replacing manual file processing and helping ensure datasets were available within scheduled reporting windows.
- Implemented automated data-quality and validation checks (null values, duplicates, record counts, invalid dates) to enforce schema integrity and catch bad data before it reached downstream reports.
- Optimized high-volume SQL transformations by improving join and filtering logic, reducing processing time on key queries.
- Analyzed and corrected frequent pipeline challenges, such as failed jobs, schema alterations, and data inconsistencies, bolstering overall pipeline reliability.
- Partnered directly with analysts and business stakeholders to translate ad hoc and recurring reporting requirements into validated, structured datasets.
- Documented six production pipelines (source mappings, transformation logic, schedules, recovery steps), streamlining troubleshooting and team handoffs.
- Created efficient Python/SQL solution to validate recurring CSV export from operations team, normalizing column names and date formats, discarding duplicate entries, and marking incomplete records ahead of reporting
- Designed a SQL-based pipeline-monitoring table capturing run-level metadata (record counts, success/failure status, error messages, processing time, timestamps), feeding a Power BI dashboard that gave the team visibility into daily data loads and pipeline health.
- Containerized a Python-based ETL pipeline with Docker to eliminate environment-dependent inconsistencies, packaging code, dependencies, and runtime settings for consistent local testing and scheduled deployment.
Cloud Engineer
LearnEdze Networks Pvt Ltd
04.2021 - 07.2023
- Built a hybrid data platform using Amazon Redshift, DynamoDB, and Amazon S3 to centralize application data for reporting, analytics, and operational use.
- Reworked backend processes into modular, event-driven workflows that made recurring ingestion and downstream processing easier to manage.
- Designed ETL workflows that extracted data from application databases and files, applied validation and transformation rules, and loaded standardized results into relational tables.
- Developed REST API integrations that retrieved data from internal and third-party services, validated JSON responses, and stored standardized records for application use.
- Added authentication, retry handling, response validation, and structured logging to improve the reliability of external integrations.
- Wrote SQL queries for data extraction, cleansing, aggregation, duplicate identification, reconciliation, and operational reporting.
- Tuned SQL queries and ETL workflows to improve processing times and make transformed data available sooner to downstream teams.
- Integrated application and data workloads across AWS and DigitalOcean to support specialized learning environments and operational processes.
- Worked with product, development, and testing teams to clarify source fields, resolve data discrepancies, and improve backend data flows.
Education
Bachelor of Science - Electronics And Communications Engineering
CMR Institute of Technology
BangaloreSkills
## TECHNICAL SKILLS **Data Engineering & ETL/ELT:** ETL/ELT Pipeline Design, Batch Processing, Recu
Certification
AWS Certified Data Engineer – Associate
Timeline
Data Engineer
PamTen
06.2025 - Current
Data Engineer
HCA
09.2023 - 05.2025
Cloud Engineer
LearnEdze Networks Pvt Ltd
04.2021 - 07.2023
Bachelor of Science - Electronics And Communications Engineering
CMR Institute of Technology