Data Engineer — Snowflake · Databricks · PySpark · AWS / Azure
Building cloud-native pipelines that hold up at scale.
Data Engineer with 4 years building cloud-native ELT pipelines and Lakehouse platforms across healthcare and insurance. Migrated 50TB+ of legacy data to Snowflake and cut multi-hour batch windows to under three hours, processing 100M+ records daily on PySpark, Airflow, and dbt. Strong in dimensional modeling, Kafka streaming, and Terraform-driven CI/CD, with hands-on experience extending pipelines for LLM / RAG retrieval use cases.
// typical pipeline shape
50TB+
Legacy data migrated to Snowflake
100M+
Records processed daily via PySpark / Airflow / dbt
8h → <3h
Clinical analytics batch window at UnitedHealth Group
~30%
Snowflake compute cost reduction from warehouse tuning
Experience
2021 — present
Data Engineer
UnitedHealth Group
- Built production ELT pipelines on Snowflake and PySpark integrating claims and provider data, reducing a key clinical analytics window from roughly 8 hours to under 3.
- Tuned Snowflake warehouses through clustering keys, micro-partition pruning, and right-sized compute, lowering query cost on the highest-spend workloads by an estimated 30%.
- Extended a document-ingestion pipeline with vector embeddings and LangChain to support an internal LLM-based clinical search prototype used by analytics teams.
- Automated deployments with Terraform and GitHub Actions, standardizing dev, staging, and production environments and shrinking release cycles.
- Implemented HIPAA-aligned access controls (RBAC, dynamic data masking) across patient and claims datasets, passing internal security review with no critical findings.
Data Engineer
MetLife
- Re-engineered legacy ETL into PySpark distributed jobs across underwriting and claims, roughly halving daily batch runtime and removing the primary pipeline bottleneck.
- Delivered Azure Data Factory, Snowflake, and Databricks pipelines supporting analytics over 25M+ insurance policies, cutting reporting turnaround for downstream teams.
- Authored dbt transformation models using star-schema dimensional design with SCD Type-2 handling, plus dbt and Great Expectations tests that raised data-quality pass rates across claims analytics.
- Built Kafka streaming for fraud and claims event monitoring, moving event processing from batch latency to near-real-time and enabling proactive fraud flags.
- Automated cloud infrastructure with Terraform and Azure DevOps CI/CD, reducing manual provisioning effort across multiple data engineering teams.
- Engineered feature pipelines for predictive underwriting and retention models, accelerating data-science model development cycles.
Data Analyst
Accenture
- Analyzed 50TB+ of legacy Oracle, SQL Server, and Hadoop data migrated to Snowflake for Fortune 500 clients, identifying reporting gaps and improving query performance by an estimated 60%.
- Built and maintained SQL-based reporting and reconciliation frameworks across AWS ELT pipelines (Glue, S3, Airflow), supporting batch reporting cycles compressed from roughly 12 hours to under 3.
- Performed row-level data validation and reconciliation during CDC cutover from legacy systems to Snowflake, ensuring data accuracy and parity for downstream analytics.
- Designed reusable data profiling and validation templates that cut new-source onboarding effort by an estimated 40%, speeding up analysis turnaround across migration programs.
- Analyzed Snowflake query and partitioning performance to identify cost drivers, contributing to an estimated 30% reduction in cloud processing spend.
- Automated reconciliation and validation reporting in Python and SQL, replacing manual verification across millions of migrated records.
Skills
10 categoriesLanguages
Python, SQL, PySpark, Scala, Shell Scripting
Warehousing & lakehouse
Snowflake, Databricks, Delta Lake, Apache Iceberg, dbt, dimensional modeling (star schema, SCD Type-2)
Big data & streaming
Apache Spark, Kafka, Airflow, Hadoop, Hive
AWS
S3, Glue, Lambda, EMR, Step Functions, Athena, IAM, CloudWatch, Redshift
Azure
Data Factory (ADF), ADLS Gen2, Synapse, Functions, Key Vault, DevOps
Modern data / AI
LLM data pipelines, RAG, vector databases (Pinecone, Weaviate), embedding pipelines, LangChain
Databases
Snowflake, Oracle, SQL Server, PostgreSQL, MongoDB, DynamoDB
DevOps & IaC
Docker, Kubernetes, Terraform, Jenkins, GitHub Actions, Git, CI/CD
Data quality & governance
dbt tests, Great Expectations, RBAC, data lineage, data masking, HIPAA controls
BI & visualization
Power BI, Tableau, Looker
Education
Master of Science, Computer Technology
Eastern Illinois University
2024
Bachelor of Engineering, Computer Science
REVA University
2021