Data Architect

Euna Solutions•Atlanta, GA

22h•Hybrid

About The Position

Euna Solutions is building a modern data foundation and is seeking a Data Architect to define it. This role will set the reference architecture, engineering patterns, and governance posture for an entire data program. The architect will also be hands-on in building alongside the team when necessary. This position is ideal for someone who thrives on designing solutions correctly from the outset and creating durable patterns for engineers to implement.

Requirements

Senior data architecture experience with production AWS systems, including S3-based lake or lakehouse designs and a governed analytical layer.
Deep fluency with medallion or layered data architectures and the governance controls that ride across them.
Experience designing contextual or semantic data layers that reduce GenAI token consumption so downstream AI features run faster and cost less.
Hands-on knowledge of managed AWS data services — Glue, Athena, Lake Formation, DMS or equivalent — with a strong bias toward managed over self-run.
Proven experience designing for data classification, PII handling, access control, lineage, and residency, ideally in a regulated or public sector context.
The judgment to build just enough: you can define a blueprint without over-engineering it, defer what the backlog doesn’t yet need, and hand off clean, documented patterns a small team can execute against.

Nice To Haves

Familiarity with dbt and warehouse-side modeling (e.g. Redshift).
Familiarity with low-code orchestration such as n8n.
Experience with Canadian data residency requirements and multi-region AWS deployments.
A track record of standing up governance from day one rather than retrofitting it later.

Responsibilities

Define a managed AWS reference architecture that delivers reliability without requiring a large team to operate it.
Specify the layered data model — raw to cleansed to curated to a fully safe analytical layer that holds no sensitive data — so the program never becomes a data swamp.
Establish governance across every layer: data classification, constituent PII handling, access control, lineage, and residency.
Create reusable engineering patterns for capture, land-and-store, transform-and-serve, and govern, so every team building data products assembles them consistently instead of reinventing plumbing.
Design loose coupling from operational systems using change-data-capture or scheduled extracts, without creating hard dependencies on their roadmaps.
Set the two-speed guardrail — make the governed, pattern-based path the default and define the narrow criteria under which a low-code workflow is permitted.
Drop into delivery and build data products yourself when the team needs the extra hands.