Pressw

Senior Engineer – Data Engineering Lead

Remote, United States · Senior

Sponsorship not specifiedDetected 76 days ago

PythonPostgreSQLAWSKafkaData EngineeringCompliance5G/LTEHIPAACollaboration

About the role

ABOUT PRESSW PressW is where some of the world's most ambitious companies come to actually ship AI.
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.
Managed AI is our MSP, purpose-built for institutional investors: private equity firms, venture capital funds, asset managers, and capital markets clients.

Lead the database schema assessment early in the engagement.
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.
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.
Own database performance tuning during the migration: read replica strategy, capacity projections, and managing elevated load during the dual-system period.
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.

7+ years of data engineering experience.
Hands-on experience with a large migration or rebuild where two systems ran in parallel and data had to stay consistent across both.
Production AWS experience, particularly Aurora and related data services.
Must have shipped meaningful work in the last 2 years.
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.
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 old schema remains untouched.
Healthcare data, HIPAA, PHI handling.
Event-driven architectures and streaming (Kafka). One phase of this engagement replaces a legacy message bus with Kafka.
Data migration is where rebuilds live and die. You own the most critical technical path on the engagement: the translation layer, the target schema, and the cutover. Small team, high autonomy, real stakes.

Experience with additive-only migration strategies (no destructive schema changes while legacy system is still active).
Event-driven architectures and streaming (Kafka).
One phase of this engagement replaces a legacy message bus with Kafka.
SQLAlchemy and Alembic experience.
AI-assisted development workflows (Claude Code, agent frameworks, MCP).
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.

no normalization, circular references, denormalized god tables, column-based multi-tenancy, and inconsistent naming.
New code never sees any of this.
Instrument data quality, lineage, and audit capabilities sufficient for a healthcare context.

This listing is sourced directly from Pressw's careers page and normalized into a canonical job model.