Data Architect

Further•Dallas, TX

3d•Hybrid

About The Position

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! Data Architect 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 at night. You will sit in our headquarters in Dallas, TX on a hybrid schedule. The team is in office on Tuesdays, Wednesdays and Thursdays.

Requirements

10+ years of data engineering, data platform, or database architecture experience, with at least 3 years owning the architecture.
Production experience with event-sourced systems.
Deep Postgres expertise: schema design, indexing, query plan analysis, RLS.
Experience with streaming platforms: Kafka, Redpanda, Pulsar, or Kinesis.
Production analytical data infrastructure experience: warehouse design (Snowflake, BigQuery, Databricks, ClickHouse) or modern OLAP patterns.
Hands-on AI/ML data infrastructure experience: vector databases (pgvector, Pinecone, Weaviate, Qdrant), embedding pipelines, retrieval patterns.
Strong SQL skills.

Nice To Haves

Drizzle, Prisma, or another modern TypeScript 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.
Open-source work or public writing on data architecture.

Responsibilities

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.
Develop the canonical event envelope and schema evolution strategy, including versioning, upcasters, projection rebuilds, and adding fields without breaking history.
Design and manage the transactional data layer, including Postgres schema design, tenant isolation via row-level security, indexing strategy, query patterns, and migration discipline.
Define the read-side architecture, including projection design, materialized views, and the semantic layer for downstream analytics and AI consumption.
Build and maintain the AI/ML data infrastructure, including vector stores, embedding pipelines, retrieval-augmented generation patterns, feature stores, and data lineage.
Implement data governance strategies, including lineage, retention, deletion, and audit trails for customer data.
Create and maintain the operational playbook for data layer issues, including projection rebuilds, replay procedures, schema migrations, vector index maintenance, and capacity planning.