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Eric Shaw

@ericshaw1

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Senior data engineer building streaming, lakehouse, and analytics platforms at petabyte scale.

United States

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What I'm looking for

I’m looking for a role where I can lead high-scale data platform work—designing streaming ingestion, lakehouse migrations, and self-serve analytics—while partnering across teams to ship production code that cuts latency and cloud cost.

I’m a data engineer with 10 years designing and operating petabyte-scale data platforms for high-growth SaaS companies. I bring deep experience across streaming ingestion, lakehouse architecture, and self-serve analytics, and I’ve been most recently leading a platform team through a 10x jump in event volume.

At Oznolo, I re-architected the event pipeline from batch Airflow to a Kafka, Flink, and Iceberg streaming stack—taking event-to-query latency from 45 minutes to 12 seconds at 80M to 820M events/day. I also authored a lakehouse migration RFC moving 340TB of Snowflake hosted data to an Iceberg on S3 lakehouse, cutting steady-state warehouse spend by $1.1M/yr with no regression in P95 query latency. I build production-ready systems end to end—from ingestion to the semantic layer—while enforcing transformation quality with dbt CI checks and a Great Expectations-based data quality framework.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Oznolo

Apr 2022 - Present (4 years 2 months)

Led a 4-engineer data platform team, owning the end-to-end roadmap from ingestion through the semantic layer for analytics, ML features, and finance reporting. Re-architected event ingestion to Kafka/Flink/Iceberg and cut event-to-query latency from 45 minutes to 12 seconds while scaling volume 10x (80M to 820M events/day), plus migrated 340TB to an Iceberg lakehouse on S3 to reduce warehouse spen

Kafka Apache Flink Apache Iceberg AWS S3 DBT Great Expectations Airflow Data Quality Lakehouse Architecture

Senior Data Engineer

Periscope Data

Feb 2019 - Mar 2022 (3 years 1 month)

Built a multi-tenant query cache layer serving ~120M dashboard queries/month across 900 workspaces, reducing median load time from 4.1s to 680ms. Led zero-downtime migration of 11B rows from Postgres to ClickHouse and implemented CDC on a unified Debezium/Kafka platform, cutting P1 incidents from 7 per quarter to 1 per quarter.

Clickhouse PostgreSQL SQL Debezium Kafka Caching

Data Engineer

Stitch

Jul 2016 - Jan 2019 (2 years 6 months)

Developed and maintained 22 production ELT source connectors (e.g., Salesforce, HubSpot, Marketo, Stripe, Shopify) handling ~6B rows/day. Rebuilt the job scheduling service for 900k nightly pipeline runs, reducing stuck-job incidents by 85%, and helped land enterprise renewals by shipping an integration layer into downstream data fabric tooling (cited in 9 of the top 20 renewals that year).

Python ELT Connectors Salesforce HubSpot Marketo Stripe Shopify job scheduling