Senior Engineer – Data Engineering Lead

About PressW

PressW is where some of the world's most ambitious companies come to actually ship AI.

We're redefining how businesses harness AI by building real systems for real clients, not just selling slides about what's possible. We're a team of three exited AI founders and a group of Applied AI Engineers based in Austin, and we've been heads-down for the last few years building this firm into something we're proud of: more than 70 production AI solutions shipped, 40+ clients across seven industries, and over $50M in measurable profit gains delivered.

We work on the bleeding edge of what AI can do, guide our clients to the right solutions for their business, and ship the infrastructure they run on.

We sell across three divisions.
Managed AI is our MSP, purpose-built for institutional investors: private equity firms, venture capital funds, asset managers, and capital markets clients. We map how a firm actually works, deploy custom agents into the workflows that consume deal teams' time (reading deal documents, drafting IC memos, scoring targets, triaging inbound, structuring offers, monitoring portfolio companies), and keep evolving those systems as models and firm needs change.

AI Engineering embeds our teams directly with clients to build and harden production AI systems.
AI Transformation handles roadmap and readiness work, helping leaders figure out what to build, in what order, and how to prepare for it.

You will own the data path for PressW's healthcare scheduling platform rebuild (codename Phoenix), reporting to the Principal Engineer. Decomposing a large monolith without breaking the business is primarily a data problem. The legacy database is multi-TB with a degraded schema: no normalization, circular references, denormalized god tables, column-based multi-tenancy, and inconsistent naming. New code never sees any of this. You build the translation layer that makes that possible, design the clean target schema, and execute the cutover that moves 1,000+ customers from old to new.

• Lead the database schema assessment early in the engagement. Map the full legacy data model, identify structural problems (circular references, denormalized tables, missing constraints), and classify tables by access patterns. This assessment shapes every phase that follows.

• Design the clean target schema in collaboration with the Backend Lead and Principal Engineer. Naming conventions, tenancy patterns, partitioning strategy. This is the schema the client lives on after the engagement.

• Build and own the translation layer between the legacy schema and the new backend:
  PostgreSQL views and materialized views for reads, write mapper modules for writes. The new code works with clean data models while the old schema remains untouched underneath. You design this layer, build it, benchmark it against production-scale data, and keep it performant throughout the migration.

• Own the data contract between old and new systems. The Backend Lead builds against the interfaces you provide. You are responsible for ensuring data consistency while both systems are running.

• Plan and execute the per-tenant data cutover from legacy schema to the target schema, with verification gates at each stage and rollback safety throughout.

• Own database performance tuning during the migration: read replica strategy, capacity projections, and managing elevated load during the dual-system period.

• Instrument data quality, lineage, and audit capabilities sufficient for a healthcare context.

• Mentor engineers on the team on data fundamentals.

• 7+ years of data engineering experience. Strong Python and SQL.

• Deep PostgreSQL expertise beyond writing good queries. You have built and maintained views, materialized views, and complex schema migration strategies in production. Aurora PostgreSQL experience strongly preferred.

• Hands‑on experience with a large migration or rebuild where two systems ran in parallel and data had to stay consistent across both.

• Experience designing schemas from degraded or legacy starting points, with an understanding of how to build translation layers that let new code work cleanly while the…