Vedhasree B

Data Engineer

United States4 yrs 2 mos experience

Most Likely To Switch

Key Highlights

Expert in building scalable cloud-native data pipelines.
Proficient in HIPAA-compliant data governance.
Skilled in optimizing data processing for analytics.

Stackforce AI infers this person is a Data Engineer specializing in cloud-native solutions for Healthcare and Telecom industries.

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Skills

Core Skills

Data EngineeringCloud Data InfrastructureCloud Data EngineeringLakehouse ArchitectureBig Data EngineeringEtl FrameworksStreaming Data Pipelines

Other Skills

ADFADLSAWS GlueAWS LambdaAirflowAmazon S3Amazon Web Services (AWS)Apache AirflowApache BeamApache KafkaAzure Data FactoryAzure DatabricksAzure Synapse AnalyticsBigQueryCloud Storage

About

I’m a Data Engineer specializing in building reliable, scalable cloud-native pipelines, Lakehouse architectures, and high-quality data platforms across AWS, Azure, and GCP. I work across ETL/ELT frameworks, Spark-based processing, and modern analytics ecosystems supporting healthcare, enterprise BI, and research-driven environments. I build scalable and reliable batch and streaming data infrastructure that powers reporting, actuarial models, analytics, and data-driven decision-making across cross-functional teams. My work includes AWS Glue pipelines, ADF orchestration, PySpark transformations, Delta Lake medallion architecture, Redshift/Snowflake optimization, and HIPAA-aligned data governance for PHI/PII. What I enjoy most is solving complex data challenges to improve pipeline performance, reducing processing cost, implementing quality controls, and designing systems that scale across millions of records. Technical Focus: AWS • Azure • GCP • PySpark • SQL • Databricks • Airflow • Kafka • Delta Lake • Snowflake • Redshift • BigQuery • Data Quality • Governance • Lakehouse Design I’m always open to discussing opportunities where I can help teams build smarter, faster, and more reliable data systems.

Experience

Unitedhealth group

Data Engineer

Jan 2025 – Present · 1 yr 2 mos

Building HIPAA-compliant data infrastructure powering actuarial analytics, clinical insights, and enterprise reporting.
Engineered ELT pipelines using Glue, PySpark, and Lambda to process high-volume clinical, claims, and eligibility datasets.
Designed S3-based data lake layers with schema evolution, optimized partitioning, and automated ingestion.
Developed and tuned Redshift transformations using distribution/sort keys, pruning, and WLM policies to accelerate analytical workloads.
Implemented validation, anomaly detection, referential checks, and reconciliation to strengthen data quality for analytics teams.
Built metadata-driven ingestion with lineage, auditability, and PHI/PII governance aligned with HIPAA controls.
Optimized PySpark transformations through secure IAM-based design, encryption, caching, and efficient storage formats.
Delivered curated marts for actuarial modeling, provider intelligence, utilization management, and enterprise reporting.
Partnered with clinical analytics, BI, and product teams to translate healthcare workflows into scalable pipelines.

AWS GluePySparkLambdaS3RedshiftHIPAA+3

National science foundation (nsf)- arsi

Cloud Data Engineer

May 2024 – Nov 2024 · 6 mos

Developed cloud-native pipelines and Lakehouse layers supporting academic research, ML experimentation, and statistical analysis.
Built ETL/ELT workflows using ADF, Databricks, and PySpark for multi-format research datasets.
Implemented a Delta Lake–based medallion architecture (Bronze/Silver/Gold) on ADLS for analytics and ML pipelines.
Automated ingestion across REST APIs, Blob Storage, and SQL sources with monitoring and alerting.
Modeled Synapse analytical layers using star-schema and incremental patterns for fast academic querying.
Designed Tableau dashboards enabling researchers to analyze trends, experiments, and statistical outputs.

ADFDatabricksPySparkDelta LakeTableauCloud Data Engineering+1

University of arkansas at little rock

Graduate Research Assistant

Sep 2023 – May 2025 · 1 yr 8 mos · Arkansas, United States

Wipro

Big Data Engineer

Aug 2022 – Jul 2023 · 11 mos · Hyderabad, India

Worked across enterprise data platforms to build scalable ETL frameworks, analytical models, and production pipelines.
Engineered large-scale ETL pipelines using PySpark, Databricks, and AWS Glue for multi-terabyte enterprise data.
Designed Snowflake and Redshift dimensional models (star/snowflake) supporting BI, finance, and operations teams.
Built Airflow-orchestrated ingestion for APIs, RDS, S3, and real-time streaming sources.
Improved PySpark performance via partitioning, broadcast joins, caching, and compression strategies.
Processed datasets using Hadoop, Hive, and HDFS to support regulatory and compliance reporting.
Executed Spark workloads on EMR for cost-efficient high-volume transformations.
Developed SQL pipelines and stored procedures across PostgreSQL, Snowflake, and Redshift.
Integrated quality checks (null profiling, anomaly detection, referential validation) into Airflow pipelines.

PySparkDatabricksAWS GlueSnowflakeRedshiftAirflow+2

Verizon

Data Engineer

Nov 2021 – Aug 2022 · 9 mos · Chennai, India

Built cloud and streaming-based pipelines supporting telecom analytics, network performance monitoring, and BI reporting.
Developed GCP-native ETL pipelines using BigQuery, Dataflow, and Cloud Storage.
Implemented Pub/Sub streaming ingestion for near real-time network event processing.
Optimized BigQuery SQL using partitioning, clustering, materialized views, and window functions.
Created reusable Dataflow templates for batch and streaming data integration.
Built automated schema validation, null checks, and anomaly detection for telecom reporting.
Supported on-prem to GCP migration by redesigning ingestion and storage patterns.
Delivered curated datasets powering Looker Studio dashboards for telecom operations.