Data Engineer – Data Platform (AI-Enabled)

Petal

1d•Remote

About The Position

Petal is looking for an AI-First Data Engineer to join a small platform team building the next generation of our data platform, enabling advanced BI and enabling AI workflows across the organization. This is not a traditional data engineering role. We are looking for someone who has already made the shift to AI-first development and can show, through real examples, how they use AI to deliver faster, better, and with greater leverage. The successful candidate will help transform Petal into an AI-first organization by combining strong data engineering fundamentals with practical, hands-on expertise in AI-assisted delivery. They will be expected to work, teach, influence, and lead. Our data platform serves customers, product and development teams, and internal business users. The vision is to make the platform AI-ready: governed, trusted, semantically rich, and designed for consumption by both humans and AI systems. Candidates must be able to demonstrate how they use AI tools in real delivery work, not just experimentation.

Requirements

Strong data engineering experience, including SQL, data modelling, data processing pipelines, testing, documentation, and data integrations in Production environments.
Ability to build high-quality data products with clear ownership, definitions, tests, lineage, and governance.
Strong understanding of Ai-ready semantic modelling, metrics, business definitions, secure API data exposures, and self-service analytics.
A platform / product mindset rather than one-off solutions, supported by expertise in cloud infrastructure and DevOps, data architecture, and in building reusable capabilities.
Comfort working in a small team where ownership, initiative, and communication matter.
Demonstrable experience using AI tools in real engineering work, such as Cursor, Claude, ChatGPT, Copilot, or similar.
Ability to teach, coach, and influence others as Petal moves toward AI-first ways of working.
Experience in building Machine-Learning pipelines for enterprise-class software solutions.

Nice To Haves

Experience in regulated, privacy-sensitive, or healthcare-related data environments would also be helpful.

Responsibilities

Partner with product, engineering, customer-facing teams, and business users to build trusted, well-modelled, AI-ready data products for customers, products, and internal use cases.
Help define and implement a semantic layer that makes data easier for humans, BI tools, and AI systems to consume.
Support a federated BI model where platform consumers can safely build their own insights.
Use AI tools daily to accelerate development, testing, documentation, debugging, analysis, and design.
Help establish practical AI-first development patterns for the team.
Lead by example in using AI to increase productivity while maintaining quality, security, governance, and trust.
Ownership extends beyond development and includes the ongoing operation of services.
Candidates should be ready to participate in our Enterprise On-Call and Incident Response Process.