Data Architect, BI Analytics Platform

🏛️ Define what a trustworthy analytics platform looks like.

Design the architecture (ex: Medallion - raw Bronze, conformed Silver, business-ready Gold) and set the modeling standards, naming conventions, and patterns every team follows.

🧬 Make every number traceable.

Establish end-to-end, column-level lineage from source system to dashboard, backed by a maintained catalog and business glossary, so any metric can be explained and audited.

🔒 Handle PII by design.

Classify sensitive data and build the controls that protect it without slowing analysts down: tagging, masking, row access policies, least-privilege RBAC, and clear retention and deletion rules.

⏱️ Turn data reliability into commitments.

Define SLAs, SLOs, and freshness targets for every data product, and design the monitoring and alerting that catch a breach before consumers do.

🔀 Design how data flows.

Architect ingestion, transformation, and orchestration patterns that are idempotent, recoverable, and cost-aware, including backfills, late-arriving data, and failure handling.

🧱 Evolve schemas without breaking people.

Own schema design review, versioning, and change management so the platform can grow while downstream reports keep working.

🧭 Own the architecture.

Be accountable for schema design and oversight across every layer, approving changes through design review and keeping the platform coherent as it grows.

🤝 Partner with the business and with engineers.

Translate reporting needs from analysts, BI developers, and business leaders into durable data models, and work with Security, Legal, and Compliance on audits and access reviews.

🧪 Hold the platform to a high bar.

Require version control, code review, CI/CD, automated tests, and infrastructure as code for every change, and enforce data quality contracts at each layer boundary.

📝 Make decisions visible.

Write architecture decision records and design docs that state the trade-offs plainly and invite challenge.

🧑‍🏫 Raise the technical bar around you.

Mentor data engineers and analytics engineers through design review, written guidance, and hands-on collaboration on the problems they own.

📐 Have deep data architecture expertise.

You bring 8+ years in data engineering, architecture, or warehousing, including 3+ years as the accountable architect, with expert SQL and working fluency in Python.

❄️ Know Snowflake and the Medallion pattern inside out.

You've run Snowflake (or equivalent) in production at scale, including RBAC, masking and row access policies, object tagging, resource monitors, and cost management. Experience with dbt, Terraform, and an orchestrator like Airflow or Dagster is a plus.

🗂️ Model data with intent.

You're expert in dimensional (Kimball) and normalized modeling, ideally Data Vault too, and know when each one fits.

🛡️ Have a track record with PII and regulated data.

You've handled classification, minimization, masking or tokenization, retention, and deletion in line with regulations such as CCPA, GDPR, or SOC 2.

🔎 Believe in lineage and observability.

You've defined and met data SLAs, with the monitoring, alerting, and incident response to back them. Experience with catalog tools such as Atlan, Alation, Collibra, or DataHub is a plus.

🛠️ Engineer with discipline.

Git workflows, code review, CI/CD, automated testing, and environment promotion are second nature. You favor automation over manual process and written decisions over tribal knowledge.

💬 Lead through influence.

You can say no to a risky shortcut, explain why, and offer a workable alternative, and you earn trust from engineers and business stakeholders alike.

🔄 Adapt quickly to new tools and technologies.

Your judgment and fundamentals matter more than experience with any one tool. Great engineers learn new technologies. Great systems thinking is harder to teach.