Senior Python Data Engineer Healthcare Interoperability

SAI ANNA

Senior Python Developer Data Engineer Healthcare Data & Interoperability

*************@*****.*** +1-203-***-**** linkedin.com/in/a-rangasai-ba462a25a

PROFESSIONAL SUMMARY

·Senior Python Developer and Data Analyst with 9+ years of experience designing, developing, and supporting enterprise applications, data pipelines, APIs, analytics solutions, and healthcare interoperability platforms.

·Strong hands-on expertise in Python 2.x/3.x, FastAPI, Flask, Django, Pandas, NumPy, PySpark, SQL, and PL/SQL, with experience building scalable backend services and automated data-processing solutions.

·Extensive experience designing ETL/ELT pipelines, RESTful APIs, microservices, batch processing, and event-driven architectures for healthcare, public-sector, financial, and enterprise applications.

·Strong healthcare domain expertise across HL7 v2.x/v3, FHIR, CCD/CCDA, HEDIS, ELR, eCR, claims, eligibility, provider, member, laboratory, and public-health surveillance data.

·Experienced with AWS, Azure, and GCP, including Lambda, S3, Glue, RDS/Aurora, CloudWatch, Azure Data Factory, Synapse, Cloud Functions, Pub/Sub, BigQuery, and Cloud Run.

·Advanced experience with Snowflake, Databricks, Delta Lake, dbt, Apache Airflow, Spark SQL, Kafka, RabbitMQ, and cloud-native data engineering architectures.

·Strong database development background using PostgreSQL, Oracle, SQL Server, MySQL, DB2, MongoDB, DynamoDB, Redis, and InterSystems Cache/IRIS, including performance tuning, indexing, stored procedures, and reconciliation.

·Experienced in data validation, data quality, reconciliation, root-cause analysis, exception handling, logging, monitoring, and production troubleshooting across high-volume enterprise systems.

·Hands-on experience developing Generative AI and RAG solutions using Python, LLM APIs, embeddings, vector databases, semantic retrieval, and secure AI-enabled backend services.

·Proven ability to collaborate with business stakeholders, healthcare SMEs, epidemiologists, architects, DBAs, QA, DevOps, and engineering teams through requirements analysis, development, UAT, deployment, and production support.

TECHNICAL SKILLS

Programming

Python 2.x/3.x, SQL, PL/SQL, JavaScript ES6+, TypeScript, React.js, Angular.js, Perl, Shell/Bash, PowerShell, HTML5, CSS3, XML, JSON, YAML

Python / APIs

Pandas, NumPy, PySpark, SciPy, Matplotlib, BeautifulSoup, Requests, Jinja2, Pydantic, Django, Flask, FastAPI, Pyramid, web2py, REST, SOAP, GraphQL (basic), microservices

Healthcare

HL7 v2/v3 (ADT, ORM, ORU, VXU, RDE), FHIR REST APIs/resource mapping, CCD/CCDA, HEDIS, ELR, eCR, EPI/MPI, LOINC, SNOMED, ICD-9/10, CPT, HCPCS, HIPAA, HITECH

Integration

Orion Health Rhapsody, Symphonia Mapper, EDI Explorer, NextGen Rosetta, Kafka, RabbitMQ, AWS SQS/SNS/Kinesis, GCP Pub/Sub

Cloud / DevOps

AWS Lambda, S3, EC2, RDS/Aurora, Glue, IAM, CloudWatch, VPC, ELB, API Gateway, Route53, CloudFormation; Azure Functions, API Management, Synapse, Data Factory, DevOps; GCP BigQuery, GKE, Cloud Run, Cloud Functions, Cloud Storage; Docker, Kubernetes, Terraform, Jenkins, GitHub Actions, CI/CD

Data / ETL

Snowflake, Databricks, Delta Lake, dbt, Spark SQL, Hadoop, Airflow, Prefect, Informatica, SAS, ETL/ELT, batch/streaming, Parquet, ORC, Avro

Databases

PostgreSQL, Aurora PostgreSQL, SQL Server, Oracle, DB2, MySQL, Teradata, Informix, InterSystems Cache/IRIS, MongoDB, Cassandra, Redis, DynamoDB; indexing, partitioning, stored procedures, query optimization

Testing / BI / Tools

Pytest, UnitTest, Behave, Robot Framework, Jest, ELK/Elasticsearch/Logstash/Kibana, Power BI, Tableau, JIRA, Confluence, Git, GitHub, GitLab, Bitbucket, VS Code, PyCharm, Linux/Unix, Agile/Scrum, UAT, defect tracking

PROFESSIONAL EXPERIENCE

NJ Department of Health, Trenton, NJ

Jan 2026 - Present

Senior Python Developer & Data Analyst

Developed scalable Python-based ETL and data-processing pipelines to ingest, cleanse, transform, validate, and reconcile laboratory, clinical, case-reporting, and public-health surveillance data.

Designed reusable solutions using Python, Pandas, NumPy, SQL, FastAPI, and Flask, reducing manual data-processing activities and improving repeatability of reporting workflows.

Developed and optimized SQL and PL/SQL queries, stored procedures, database lookups, and reconciliation routines to identify missing records, duplicates, invalid codes, and data discrepancies.

Engineered secure RESTful APIs and Python microservices to process HL7, FHIR, JSON, XML, CSV, laboratory, and public-health data across enterprise healthcare applications.

Built reusable payload validation and transformation frameworks using Pydantic, Python, Pandas, and SQL to prevent malformed or incomplete healthcare transactions from reaching downstream systems.

Supported HL7 v2.x, FHIR, ELR, eCR, MPI, PHLIP, CCD/CCDA, ADT, ORM, and ORU workflows, validating patient demographics, laboratory observations, disease codes, provider data, and reporting facilities.

Integrated Python applications with Orion Health Rhapsody, database lookup filters, transformation routes, and downstream public-health systems for reliable message processing.

Performed end-to-end root-cause analysis by tracing failed healthcare messages across source systems, Rhapsody routes, transformation layers, database procedures, staging tables, and target applications.

Implemented centralized exception handling, retry logic, structured logging, audit tracking, and timeout management, improving reliability and supportability of mission-critical interfaces.

Designed cloud-native data processing solutions using AWS Lambda, Aurora PostgreSQL, GCP Pub/Sub, Cloud Functions, Cloud Run, BigQuery, and Cloud Storage.

Optimized Aurora PostgreSQL and enterprise SQL workloads using indexes, query refactoring, execution-plan analysis, views, and stored procedures to support high-volume clinical and public-health reporting.

Developed GenAI and RAG-enabled applications using Python, LLM APIs, embeddings, and vector-search techniques for healthcare document analysis, summarization, validation, and knowledge retrieval.

Supported UAT, regression testing, integration testing, and production validation by preparing test data, validating expected results, documenting defects, and coordinating resolutions with development teams.

Collaborated with epidemiology, business, database, integration, QA, and technical teams to translate functional requirements into technical specifications, data mappings, validation rules, test scenarios, and production-ready solutions.

Environment: Python, FastAPI, Flask, Django, Pandas, NumPy, PySpark, SQL/PLSQL, PostgreSQL/Aurora, Oracle, SQL Server, DB2, Cache/IRIS, HL7, FHIR, CCD/CCDA, HEDIS, ELR/eCR, Rhapsody, AWS, Azure, GCP, Snowflake, Databricks, dbt, Airflow, Kafka, Docker, Kubernetes, Power BI, Tableau, ELK, Jira, Confluence.

UnitedHealth, USA

Jan 2023 - Dec 2025

Senior Python Developer & Data Engineer

•Developed scalable Python-based ETL and data-processing pipelines to ingest, cleanse, transform, validate, and reconcile laboratory, clinical, case-reporting, and public-health surveillance data.

Designed reusable solutions using Python, Pandas, NumPy, SQL, FastAPI, and Flask, reducing manual data-processing activities and improving repeatability of reporting workflows.

Developed and optimized SQL and PL/SQL queries, stored procedures, database lookups, and reconciliation routines to identify missing records, duplicates, invalid codes, and data discrepancies

Engineered secure RESTful APIs and Python microservices to process HL7, FHIR, JSON, XML, CSV, laboratory, and public-health data across enterprise healthcare applications.

Built reusable payload validation and transformation frameworks using Pydantic, Python, Pandas, and SQL to prevent malformed or incomplete healthcare transactions from reaching downstream systems.

Supported HL7 v2.x, FHIR, ELR, eCR, MPI, PHLIP, CCD/CCDA, ADT, ORM, and ORU workflows, validating patient demographics, laboratory observations, disease codes, provider data, and reporting facilities.

Integrated Python applications with Orion Health Rhapsody, database lookup filters, transformation routes, and downstream public-health systems for reliable message processing.

Performed end-to-end root-cause analysis by tracing failed healthcare messages across source systems, Rhapsody routes, transformation layers, database procedures, staging tables, and target applications.

Implemented centralized exception handling, retry logic, structured logging, audit tracking, and timeout management, improving reliability and supportability of mission-critical interfaces.

Designed cloud-native data processing solutions using AWS Lambda, Aurora PostgreSQL, GCP Pub/Sub, Cloud Functions, Cloud Run, BigQuery, and Cloud Storage.

Optimized Aurora PostgreSQL and enterprise SQL workloads using indexes, query refactoring, execution-plan analysis, views, and stored procedures to support high-volume clinical and public-health reporting.

Developed GenAI and RAG-enabled applications using Python, LLM APIs, embeddings, and vector-search techniques for healthcare document analysis, summarization, validation, and knowledge retrieval.

Supported UAT, regression testing, integration testing, and production validation by preparing test data, validating expected results, documenting defects, and coordinating resolutions with development teams.

Collaborated with epidemiology, business, database, integration, QA, and technical teams to translate functional requirements into technical specifications, data mappings, validation rules, test scenarios, and production-ready solutions.

Environment: Python, FastAPI, Flask, Django, React.js, TypeScript, PySpark, SQL/PLSQL, PostgreSQL/Aurora, SQL Server, Oracle, HL7, FHIR, CCD/CCDA, HEDIS, Rhapsody, AWS, Azure, GCP, Snowflake, Databricks, Delta Lake, dbt, Airflow, Prefect, Kafka, RabbitMQ, Docker, Kubernetes, Jenkins, GitHub Actions, Power BI, Tableau, ELK.

TechSource Emerging Info Technologies Pvt. Ltd., Bangalore, India

Jan 2019 - Jul 2021

Python Developer & Data Analyst

•Designed and developed Python and PySpark ETL/ELT pipelines to process multi-million-record banking, transaction, reconciliation, and financial datasets.

•Built cloud-native processing solutions using AWS Lambda, S3, RDS, Glue, Kinesis, API Gateway, and event-driven microservices for batch and near-real-time transaction processing.

•Developed production-grade RESTful APIs using FastAPI, Flask, and Django for payment processing, transaction validation, fraud detection, risk scoring, and financial-data integration.

•Migrated legacy Informatica and SAS ETL workloads to PySpark and AWS Glue, improving documented processing performance by approximately 40%.

•Developed SQL and PL/SQL ETL routines, stored procedures, reconciliation queries, and validation logic for payments, settlements, ledger balances, disputes, and financial transactions.

•Improved database workload performance by up to 30% using indexing, bulk processing, query optimization, and execution-plan analysis.

•Automated reconciliation processes for payments, ledger balances, settlements, disputes, and transaction exceptions, improving data accuracy and audit readiness.

•Designed robust validation and exception-management frameworks to detect missing fields, invalid amounts, duplicate transactions, business-rule failures, and malformed payloads.

•Engineered real-time streaming solutions using Kafka, RabbitMQ, AWS Kinesis, and SQS to support risk, fraud, payment, and compliance processing.

•Developed Airflow DAGs, Snowflake/dbt transformations, and Databricks PySpark pipelines with automated retries, dependency management, SLA monitoring, and data-quality controls.

•Implemented secure application and data controls using OAuth 2.0, JWT, IAM, SSL/TLS, RBAC, data masking, and audit logging to support SOX and PCI-DSS requirements.

•Built GenAI/RAG solutions for KYC, AML, fraud investigation, and document analysis, reducing manual review effort by up to 45% in documented project workflows.

•Containerized Python services using Docker and Kubernetes and automated build, testing, and deployments using Jenkins, GitHub Actions, and Terraform.

•Partnered with Risk, Finance, Compliance, Product, QA, and DevOps teams to translate business requirements into scalable APIs, data pipelines, reconciliation processes, and analytical solutions.

Environment: Python, FastAPI, Flask, Django, PySpark, Spark SQL, Pandas, NumPy, PL/SQL, PostgreSQL, MySQL, Oracle, DB2, Snowflake, Databricks, Delta Lake, dbt, Airflow, Prefect, AWS, Azure, GCP, Kafka, RabbitMQ, Docker, Kubernetes, Terraform, Jenkins, GitHub Actions, Linux, ELK, Tableau, Power BI.

Teledyne Systems Pvt. Ltd., Hyderabad, India

May 2015 - Dec 2018

Python Developer

•Developed and enhanced backend application components using Python, Django, and Flask for product catalog, customer, inventory, cart, checkout, and order-processing workflows.

•Designed and maintained RESTful APIs supporting product search, shopping-cart operations, order processing, inventory updates, and application integrations.

•Developed Python-based ETL scripts to ingest, cleanse, transform, and validate product feeds, inventory files, pricing data, and transactional information.

•Wrote and optimized SQL queries and stored procedures using MySQL and PostgreSQL to support application processing, reporting, and analytics.

•Created database validation routines to verify order totals, inventory availability, transaction status, customer data, and downstream reporting accuracy.

•Supported production troubleshooting by analyzing SQL/PLSQL errors, failed transactions, data inconsistencies, application defects, and system logs.

•Assisted with database performance tuning by evaluating inefficient joins, indexes, stored procedures, and high-volume application queries.

•Integrated Python/Django backend services with relational databases and downstream analytical systems for application processing and reporting.

•Implemented Redis caching in collaboration with senior developers to reduce repetitive database access and improve application responsiveness.

•Developed logging, monitoring, and exception-handling capabilities using Python and ELK Stack to improve incident identification and production support.

•Automated Linux operational and deployment activities using Bash/Shell scripting, reducing repetitive manual tasks and improving release consistency.

•Supported containerized application deployments using Docker, Jenkins, Git/GitHub, and CI/CD processes across development and production environments.

•Assisted BI and analytics teams with reusable Tableau data sources, data marts, standardized metrics, and automated dashboard refresh workflows.

•Participated throughout the Agile/Scrum SDLC, including requirements analysis, sprint planning, development, code reviews, testing, defect resolution, release, and production support.

Environment: Python 2.7/3.x, Django, Flask, REST APIs, HTML, CSS, JavaScript, Angular.js, jQuery, SQL/PLSQL, MySQL, PostgreSQL, Redis, Python ETL, Git/GitHub, Jenkins, Docker, ELK, Linux/Ubuntu, JIRA.

CERTIFICATION

• AWS Certified Data Engineer - Credly credential: 47bfc1d6-8217-4a76-813c-4996dc534693

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