Summary
Overview
Work History
Education
Skills
Timeline
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John Peterson

Santa Clara,CA

Summary

Accomplished Senior Data Engineer with over 10 years of expertise in designing and optimizing cloud data platforms in healthcare, FinTech, and e-commerce. Specializes in Snowflake data warehousing, ELT architecture, and performance tuning for analytical workloads. Developed ACID-compliant financial pipelines and HIPAA-compliant healthcare data solutions using Snowflake, AWS, and Python, while also leveraging Azure and GCP for hybrid-cloud environments.

Overview

13
13
years of professional experience

Work History

Senior Data Engineer

Cardinal Health
Remote
02.2021 - Current
  • Architected and enhanced Snowflake-based healthcare and supply chain data platforms supporting clinical, operational, logistics, and reporting workloads, integrating raw data from APIs, flat files, EDI-style interfaces, cloud object storage, and downstream enterprise systems.
  • Designed ELT pipelines using Snowflake, AWS S3, AWS Glue, Python, and SQL to ingest and transform high-volume healthcare datasets into governed analytical models optimized for regulatory reporting, operational analytics, and executive dashboards.
  • Built clinical medallion-style lakehouse pattern for healthcare data domains, organizing raw HL7/FHIR and operational feeds into bronze, silver, and gold layers, enhancing traceability and data quality for analytics teams.
  • Developed standardization and transformation pipelines for healthcare interoperability data, including HL7-to-FHIR ingestion and normalization, enabling downstream consumers to utilize modern, analytics-friendly structures.
  • Implemented PHI de-identification and masking logic using hashing, field suppression, role-aware access patterns, and generalized attribute transformations to support HIPAA-aligned analytics without exposing sensitive patient identity data.
  • Partnered with business and data stakeholders to improve data provenance and audit trails, capturing source-to-target lineage, transformation history, and operational checkpoints so regulated datasets could be traced back to original systems during audit and compliance reviews.
  • Built automated healthcare data standardization flows supporting clinical terminology normalization and reference mapping concepts aligned with ICD-10, SNOMED-CT, and LOINC-style reporting needs, improving consistency of downstream measures and reporting logic.
  • Designed Snowflake models and transformation logic supporting quality measure aggregation and population-level reporting use cases, enabling business teams to evaluate screening, adherence, utilization, and operational health metrics more efficiently.
  • Tuned Snowflake workloads through warehouse right-sizing, clustering strategy improvements, query rewrites, pruning-aware design, and workload separation, improving performance for large-scale joins, curated marts, and dashboard refresh jobs.
  • Established reusable ingestion and transformation patterns for semi-structured data using JSON, VARIANT columns, Snowflake tasks, streams, and metadata-driven orchestration, reducing manual development effort for new data domains.
  • Enhanced production reliability through implementation of pipeline observability, load validation, data reconciliation checks, and failure alerting, increasing trust in regulated datasets for analysts and operations teams.
  • Supported secure enterprise analytics by applying role-based data access, controlled sharing models, and audit-conscious warehouse design, balancing performance with privacy and compliance requirements.

Senior Data Engineer

Fiserv
Remote
03.2016 - 01.2021
  • Designed and scaled Snowflake-centric financial data platforms supporting transaction analytics, reconciliation, risk reporting, audit workflows, and downstream operational data products in a high-security FinTech environment.
  • Engineered ACID-aware ingestion and transformation patterns for financial datasets, ensuring transaction completeness, deterministic processing, rollback-safe logic, and reconciliation-friendly lineage across sensitive money movement workflows.
  • Built robust ELT pipelines with Snowflake, Python, SQL, dbt, Kafka, and Spark to ingest high-volume transaction, settlement, account, and customer activity data from batch and event-driven sources.
  • Developed real-time and near-real-time streaming ingestion patterns for financial event processing using Kafka and Spark Structured Streaming, enabling faster fraud analysis, event enrichment, and operational monitoring of payment activity.
  • Supported real-time fraud detection scoring workflows by preparing low-latency transaction features and trusted event streams for analytics and downstream decisioning systems.
  • Implemented PCI-DSS tokenization-oriented data handling patterns, replacing or masking sensitive payment card data before landing in accessible analytical environments to enhance data security.
  • Built ledger reconciliation pipelines comparing internal transaction records with external processor, settlement, and downstream reporting feeds to identify mismatches, timing gaps, and duplicate events.
  • Designed transformation layers to support KYC / customer due diligence and risk-oriented analytics, integrating customer profile, transaction, and reference data into curated datasets used for compliance and operational review.
  • Contributed to AML-focused data engineering patterns, including entity relationship preparation and graph-friendly data outputs that could support suspicious activity analysis and multi-hop transaction investigation.
  • Developed parsing and normalization logic for financial messaging and standards-based exchange patterns, including XML-heavy and institution-oriented data interchange concepts aligned with ISO 20022-style message processing.
  • Designed Snowflake dimensional and analytical models for transaction volumes, settlement status, dispute analysis, customer behavior, and operational KPIs, improving reporting performance and streamlining transformation logic in BI layers.
  • Tuned Snowflake performance using warehouse isolation, clustering, query profile analysis, caching-aware SQL design, pruning optimization, and staged transformation decomposition, improving throughput and lowering compute waste.
  • Applied dbt-based transformation patterns to improve modularity, testing, documentation, and version control discipline for SQL models used in heavily audited financial reporting pipelines.
  • Partnered with platform and security teams to strengthen RBAC, encryption-aware design, secrets discipline, and controlled data access, improving compliance posture while maintaining usability for engineering and analytics teams.
  • Integrated Snowflake with broader cloud services and adjacent enterprise tooling, including AWS S3, Glue, Lambda, and selective Azure/Fabric interoperability, to support hybrid reporting and partner-facing analytics workflows.

Data Engineer

Chewy
Onsite
05.2013 - 02.2016
  • Built and maintained large-scale analytics and reporting pipelines for e-commerce, integrating clickstream, order, product, customer, fulfillment, and inventory data into analytical models that enabled data-driven decision-making.
  • Developed customer 360-style data models by merging customer profile, browsing, cart, order, and engagement data across multiple digital touchpoints to improve marketing, personalization, and retention analytics.
  • Engineered clickstream sessionization pipelines that transformed raw web and app events into structured user sessions, enabling funnel analytics, navigation-path analysis, and conversion measurement.
  • Built ingestion and transformation flows supporting near-real-time inventory synchronization, improving alignment between warehouse stock availability and customer-facing inventory signals.
  • Integrated product, competitor, and demand-related data into curated datasets supporting dynamic pricing and merchandising analytics, facilitating more informed pricing strategies and operational optimizations.
  • Developed foundational datasets for recommendation and product affinity analytics, incorporating customer-product interaction features and market-basket relationships to enhance personalization and cross-sell strategies.
  • Built pipelines for A/B testing and experimentation analytics, ensuring test/control traffic could be measured cleanly and tied to conversion, basket size, and behavioral metrics.
  • Implemented SQL and modeling improvements to optimize report performance, reduce duplicate business logic, and improve trust in KPI reporting used by business and operations stakeholders.
  • Worked with cloud and warehouse technologies that laid the groundwork for later Snowflake-centric patterns, including structured data modeling, secure handling of customer data, and scalable analytics engineering practices.

Education

Bachelor of Science - Computer Science

Florida Institute of Technology
Melbourne, FL
05-2013

Skills

  • Snowflake architecture
  • Virtual warehouses
  • Multi-cluster warehouses
  • Micro-partitioning
  • Clustering keys
  • Automatic clustering
  • Time Travel
  • Zero-Copy Cloning
  • Secure data sharing
  • Snowpipe
  • Snowpark
  • Tasks
  • Streams
  • Materialized views
  • External tables
  • Semi-structured data
  • VARIANT
  • Query profile analysis
  • Warehouse sizing
  • Workload isolation
  • Resource monitors
  • Result caching
  • Metadata-driven ELT
  • Data retention
  • Fail-safe
  • Access control
  • Masking policies
  • Row access policies
  • ETL
  • ELT
  • Batch pipelines
  • Streaming pipelines
  • CDC
  • Incremental data loads
  • Full refresh
  • Backfills
  • Schema evolution
  • Data contracts
  • Data alignment
  • Orchestration
  • DAG design
  • Failure recovery
  • Idempotent pipelines
  • Data standardization
  • Ingestion framework design
  • Metadata management
  • Lineage
  • Observability
  • SLA monitoring
  • Data quality validation
  • Reference data management
  • Advanced SQL
  • SQL for Snowflake
  • Python
  • PySpark
  • Spark SQL
  • Scala
  • Shell scripting
  • Dbt
  • Stored procedures
  • UDFs
  • Query tuning
  • Window functions
  • MERGE/UPSERT patterns
  • SCD Type 1/2
  • Partition-aware transformations
  • Modular pipeline design
  • AWS S3
  • AWS Glue
  • Lambda
  • Kinesis
  • EC2
  • IAM
  • CloudWatch
  • Redshift
  • RDS
  • Aurora
  • Azure Data Factory
  • Azure Synapse
  • Microsoft Fabric
  • Fabric Pipelines
  • Fabric Lakehouse
  • Power BI
  • BigQuery
  • Pub/Sub
  • Kafka
  • Kafka Connect
  • Spark Structured Streaming
  • Event-driven architecture
  • Real-time processing
  • Event ingestion
  • Stream enrichment
  • Data delivery guarantees
  • Event replay
  • Stream observability
  • HIPAA
  • PCI-DSS tokenization
  • RBAC
  • Encryption at rest
  • Encryption in transit
  • Audit trails
  • Lineage for audits
  • Data provenance
  • Secret management
  • Compliance data access
  • Dimensional modeling
  • Star schema
  • Snowflake schema
  • Conformed dimensions
  • Semantic layer support
  • Customer 360
  • Financial ledger modeling
  • Healthcare analytics
  • Quality aggregation
  • KPI modeling
  • Report optimization
  • Git
  • CI/CD
  • Jenkins
  • GitHub Actions
  • Terraform
  • CloudFormation
  • Docker
  • Monitoring

Timeline

Senior Data Engineer

Cardinal Health
02.2021 - Current

Senior Data Engineer

Fiserv
03.2016 - 01.2021

Data Engineer

Chewy
05.2013 - 02.2016

Bachelor of Science - Computer Science

Florida Institute of Technology

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John Peterson