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Ian Cates
Data engineer building and integrating serverless data platforms on the cloud.
About Me
Data engineer, full-stack engineer, guitarist, and traveler. Passionate about the depth of learning and growth.



I'm a data engineer and full-stack engineer with 4+ years building and integrating serverless data platforms on the cloud. My passions live at the intersection of three things: data, full-stack development, and security. Most of the work ends up touching all three at once.
The shape of what I build is intentionally end-to-end. ELT/ETL processes and DAG platforms, API services, operator portals, and the infrastructure-as-code that ships them all sit on the same canvas. The engineer who designs the pipeline writes the dashboard the data lands in. I enjoy impactful work: the kind that gets noticed and makes meaningful changes.
Beyond any specific stack, I'm genuinely fascinated by how the practice of building software is changing. The pre-AI discipline I came up on still grounds the work: careful modules, slow-and-correct review, deep observability. I treat agentic coding workflows as a first-class production system in their own right, with prompts, evals, telemetry, and review held to the same bar as everything else. Knowing when to lean on these tools and when to slow down is its own challenge, and one that only matters more as the field continues to shift.
My cybersecurity background never stopped influencing how I build. Least-privilege access, federated SSO, secret rotation, and encryption everywhere are second nature at this point. That security lens shapes every design choice in the data work: who can read this, how does it rotate, what's the blast radius.
B.S. Cybersecurity (2021) · AWS Certified Data Engineer (2024) · AWS Certified DevOps Engineer Professional (2025)
Outcomes
What the work adds up to.
5,000+
internal users powered
Dashboards and data products built on top of this platform serve 5,000+ internal users. Finance, Engineering, Product, and leadership read the same numbers instead of debating spreadsheets.
40+
interdependent ETL pipelines in production
Designed and operate 40+ production data pipelines forming an interdependent DAG of ingestion, normalization, enrichment, allocation, and marts. Heterogeneous source data converges into one queryable model behind analytics, ML, and finance reporting.
Hours, not weeks
from raw data to dashboard
Compressed reporting latency from monthly manual rollups to hourly automated refreshes. Stakeholders read live data instead of waiting on a data-team handoff.
100%
infrastructure as code
Every Lambda, IAM role, Athena view, Redshift schema, and schedule reviewed in version-controlled Terraform and applied through Terraform Cloud. No click-ops drift between dev, stage, and prod.
Projects
Real systems shipped to production.
Contact me
Open to interesting problems. Reach out about engineering roles, consulting, or anything you want a second pair of eyes on.