Senior Engineer – Data Engineering Lead

Emploi pas sur LinkedIn

🕒 il y a 9 jours

🇺🇸 États-Unis – Télétravail

⏰ Temps Plein

🟠 Senior

🚰 Ingénieur Data

🗣️🇺🇸🇬🇧 Anglais requis

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PressW

11 - 50 employés

Fondée en 2023

🤖 Intelligence artificielle

☁️ SaaS

💼 Conseil

Artificial Intelligence • SaaS • Consulting

PressW est une entreprise spécialisée en intelligence artificielle appliquée qui propose des services de transformation IA de bout en bout : stratégie, ingénierie et gestion de l'IA. En tant que partenaire officiel d'Anthropic, PressW se spécialise dans le déploiement, la gestion et l'optimisation de Claude (le modèle d'Anthropic) au sein des entreprises. Parmi les services proposés figurent le déploiement de Claude, la gestion des licences, l'analyse de l'utilisation, l'optimisation des coûts, le développement d'agents Claude personnalisés, ainsi que la sécurité, conformité et gouvernance, sans oublier le support continu. PressW s'adresse à des secteurs tels que le capital (capital-investissement, capital-risque, services financiers), la technologie, les services et l'industrie (environnements réglementés). Ils s'intègrent aux équipes de leurs clients pour évaluer la maturité face à l'IA, développer des feuilles de route, créer des systèmes d'IA de qualité production (applications, pipelines, intégrations, équipes embarquées) et proposer des services gérés et d'optimisation continus. L'entreprise met l'accent sur une mise en production rapide (90 jours en moyenne), une vaste expérience de projet (70+ projets, 30+ entreprises), et se concentre sur le retour sur investissement des initiatives IA.

Description

• 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.

🎯 Exigences

• 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 old schema remains untouched. • Production AWS experience, particularly Aurora and related data services. • Must have shipped meaningful work in the last 2 years.

🏖️ Avantages

• PressW is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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