Swathi Vangala

Data Integration Engineer with 4+ years of experience migrating and engineering complex data systems at scale. At Bitfocus, led multi-client HMIS-to-Clarity Human Services migrations — transforming millions of records with SQL, Python, and dbt on Snowflake while enforcing HUD compliance across every pipeline stage. At McKesson, reduced AWS pipeline runtime by 25% processing healthcare claims and EHR data across a medallion Delta Lake architecture in Databricks.

Brings hands-on expertise in data governance, lineage tracking, and quality validation across AWS (S3, Glue, Redshift, Lambda, Athena), Azure (Synapse, ADF), Snowflake, and Apache Airflow — from source mapping and transformation through stakeholder sign-off and source system decommission.

Data Integration Engineer at Bitfocus, Inc. (2025-03 – Present)

• Built and maintained ETL/ELT pipelines integrating data from REST APIs, SaaS platforms, databases, data warehouses, cloud storage, legacy systems, and flat files.
• Developed Python, SQL, PySpark, and dbt pipelines using incremental loads, schema mapping, deduplication, transformations, and data-quality validation.
• Built API-based integrations using authentication, pagination, JSON parsing, error handling, retry logic, and source-to-target reconciliation.
• Developed AWS Glue ETL pipelines using PySpark, processing data from Amazon S3 and databases and orchestrating workflows through AWS Step Functions.
• Troubleshot production pipelines by analyzing logs, API failures, schema changes, mapping errors, and data discrepancies and implementing root-cause fixes.
• Led 3 end-to-end HUD data migrations and supported 30+ custom migrations across databases, data warehouses, data lakes, APIs, and legacy systems.
• Developed reusable dbt models and macros supporting 500+ automated data-quality checks, field mappings, and business-rule validations.
• Managed dbt models and pipeline deployments through GitLab CI/CD across development, staging, and production environments.

Data Engineer at McKesson (2024-07 – 2025-02)

• Built AWS data pipelines (Glue + Lambda) to ingest healthcare EHR, claims, and operational data into Redshift from S3 in Parquet/Avro formats—cutting runtime by 25% through partition pruning and job-level parallelization.
• Integrated FHIR-compliant EHR and pharmacy REST APIs in Python, parsing nested JSON payloads and handling OAuth token refresh, pagination, and schema drift across vendor API versions.
• Designed a medallion Delta Lake in Databricks (Bronze → Silver → Gold) using PySpark, ORC/Parquet-backed tables, built-in data quality checks, and Unity Catalog lineage tracking.
• Authored dbt models on Redshift for incremental loads, deduplication, and business logic across pharmacy, oncology, and distribution datasets—powering Tableau dashboards for clinical and operational stakeholders.
• Used Fivetran to automate Salesforce and Workday ingestion into Redshift; complemented with Docker-containerized Python jobs for custom extraction sources.

AWS Data Engineer at Tata Consultancy Services (2021-08 – 2022-08)

• Designed and developed end-to-end cloud migration pipelines to extract millions of network and customer records from legacy and on-premises databases, transform them using AWS Glue and PySpark, and load them into Amazon S3 and Redshift.
• Built scalable ETL workflows with AWS Glue, S3, Parquet, Redshift Spectrum, and Glue Data Catalog, implementing schema mapping, data cleansing, partitioning, validation, and source-to-target reconciliation.
• Developed incremental and near-real-time CDC pipelines using AWS Lambda and Kinesis Data Streams to capture subscriber and network changes and load them into Redshift reporting tables with sub-minute latency.
• Optimized Redshift performance using distribution keys, sort keys, and WLM configuration, and provisioned Glue, S3, Redshift, Lambda, IAM, and CloudWatch infrastructure through Terraform across development, staging, and production environments.

Research Data Analyst at Vardhaman College of Engineering – VLSI Research Lab (2020-01 – 2021-07)

• Collected and curated simulation datasets for VLSI circuit research—capturing logic gate performance metrics, power consumption outputs, and switching behavior across reversible logic configurations used in published PERAM and QUEST studies.
• Cleaned and pre-processed raw hardware simulation outputs using Python and SQL—handling missing values, normalizing signal-level data, and structuring datasets into analysis-ready formats for model training and benchmarking.
• Trained and evaluated ML models using Python to predict power efficiency and error resilience of multiplier and encoder/decoder circuit designs, comparing results against baseline VLSI implementations.
• Built Power BI dashboards to visualize simulation results, model performance metrics, and circuit benchmarking data—enabling research supervisors to interactively explore power consumption trends and error-resilience outcomes across experimental configurations.
• Built data collection pipelines in Python to systematically gather and version simulation runs across experimental circuit configurations, enabling reproducible analysis supporting quantitative results in two published research papers.

Mathematics Tutor at IUPUI Mathematics Assistance Center (2024-01 – 2024-05)

• Supported 20+ undergraduates in calculus, statistics, and linear algebra; built Power BI dashboards for department supervisors to track tutoring outcomes and grade distributions.

Ph.D. in Information Technology – University of the Cumberlands) (2026-08 – 2029-12)

M.S. in Computational Data Science – Purdue University (IUPUI) (2022-08 – 2024-05)

B.Tech. in Electronics & Communications Engineering – Vardhaman College of Engineering (2017-07 – 2021-06)