Data Architect

WE’RE HIRING! If you love data and are looking for unlimited growth opportunities, we want to talk with you about joining Further.

Further is a data, cloud, and AI company whose focus is helping companies turn raw data into the right decisions. We have an award winning culture of extraordinary people. Our purpose is to enable people to thrive so that businesses can thrive. We believe that the work you do should matter - it should be meaningful to you professionally and personally, and it should have a positive impact on both you and our clients.

If this sounds exciting to you, let’s chat!

You own the data spine of the platform: the canonical event envelope, the multi-tenant transactional store, the read-side projections that power every console view and API response, and the AI/ML data infrastructure that makes the system intelligent rather than just instrumented. This is not a specialist role. We are hiring one person who has done all three surfaces at production depth and can hold the trade-offs simultaneously.

The data volume is significant, the schema evolution problem is hard, the tenant isolation requirements are unforgiving, and the AI workloads sit on top of all of it. If any of those three surfaces feel uninteresting, this isn't the role. You'll work shoulder-to-shoulder with the Solution Architect on the canonical data model and with the Senior Developer on implementation. You write the migration playbooks, the upcaster chains, the projection rebuild procedures, and the runbooks that let us sleep  will sit in our headquarters in Dallas, TX on a hybrid schedule.

The team is in office on Tuesdays, Wednesdays and Thursdays.

• 10+ years of data engineering, data platform, or database architecture experience, with at least 3 owning the architecture, not just the implementation.
• Production experience with event-sourced systems. You have personally implemented or evolved an event envelope, dealt with the upcaster chain problem, and lived with the consequences of an early schema decision.
• Deep Postgres expertise: schema design, indexing, query plan analysis, RLS. Not "I've used Postgres" but "I know what I'd do differently from the last team."
• Experience with streaming platforms:
  Kafka, Redpanda, Pulsar, or Kinesis. You understand the difference between a topic, a partition, a consumer group, and a saga, and you've designed for all of them.
• Production analytical data infrastructure experience: warehouse design (Snowflake, Big Query, Databricks, Click House) or modern OLAP patterns. You can take an event log and produce a queryable shape that analysts and AI systems can actually use.
• Hands‑on AI/ML data infrastructure experience: vector databases (pgvector, Pinecone, Weaviate, Qdrant), embedding pipelines, retrieval patterns. You have shipped a RAG system that worked and you know why most of them don't.
• Strong SQL, strong enough to be the person other engineers go to.

• Drizzle, Prisma, or another modern Type Script ORM at production depth.
• Workflow engine experience (Temporal, Cadence, Airflow) and the data implications of long‑running processes.
• Background in feature stores, ML pipelines, or model evaluation infrastructure.
• Semantic layer experience: dbt, Cube, or Malloy.
• Compliance experience: SOC 2, HIPAA, GDPR, data residency. The audit story is part of the data story.
• Open‑source work or public writing on data architecture.

• The canonical event envelope and schema evolution strategy: how events are versioned, how upcasters chain, how projections rebuild, and how we add fields without breaking history. You write the rules and enforce them in code review.
• The transactional data layer:
  Postgres schema design, tenant isolation via row‑level security, indexing strategy, query patterns, and migration discipline. Every table in a data plane carries a  and a policy. You make sure of that.
• The read‑side architecture: projection design, materialized views, semantic layer for downstream analytics and AI consumption. You decide how we move from event log to queryable shape and how that shape stays cheap to maintain.
• The AI/ML…