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Required AI Data Engineer
About the role:
We're building the intelligence layer that lets everyone ask hard questions of our data and get trustworthy answers - in plain language, in seconds. As AI Data Engineer, you own the semantic and AI-native surface of our Snowflake platform: the governed semantic layer that defines "what a metric means," the Cortex Agents that let business users query it conversationally, and the infrastructure that keeps all of it fast, reliable, and ready to scale.
This is a builder-owner role. You'll ship the semantic models, wire up the agents, and set the standard for how analysts across the company work with data. You'll also lead the Data Analyst guild - the connective tissue that keeps our hub-and-spoke analytics model coherent as it grows.
If you're excited by the space where data engineering, LLM tooling, and analytics governance meet, this role sits right in the middle of it.
What youll Own:
Snowflake semantic layer - Own the semantic layer end-to-end as the single source of truth for metrics; design semantic models, enforce naming standards, and ensure consistent metric definitions across dashboards and AI agents.
Cortex Agents - Design and deploy conversational AI agents using Cortex Analyst and Cortex Search; tune for accuracy and safety, expose through multiple surfaces (Snowflake Intelligence, Streamlit, MCP), and build evaluation harnesses to maintain quality at scale.
Data craft (Analytics guild) - Co-lead the technical track of the Analytics guild; set SQL and modeling standards, run technical enablement and code reviews, and serve as the technical authority for analysts.
Scaling data infrastructure - Improve performance, reliability, cost efficiency, and governance across the dbt/Airflow/Airbyte/Snowflake stack as data volume and query load grow; optimize warehouse sizing, medallion layers, and ingestion pipelines.
What you'll do day to day:
Model and maintain semantic views that power both dashboards and AI agents, keeping definitions versioned, tested, and certified.
Build, evaluate, and iterate on Cortex Agents - including retrieval quality, guardrails, and observability.
Extend and optimize dbt models, Airflow DAGs, and Airbyte connectors across bronze/silver/gold layers.
Partner with GTM, Finance, CS, and Product stakeholders to translate business questions into governed, reusable data assets.
Run the Data Analyst guild: standards, reviews, enablement, and tooling.
Own data quality, lineage, and cost monitoring across the Snowflake platform.
Expose data and agents through Streamlit apps, BI tools (Omni), and MCP servers for internal AI workflows.

We are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

We are looking for an experienced and visionary Data Engineer to help design, build, and scale our BI platform.
In this role, you will be responsible for developing our data analytics platform - enabling scalable data pipelines and robust data modeling to support real-time and batch analytics to provide insights that serve both business intelligence and product needs.
You will be part of the R&D team, collaborating closely with engineers, analysts, and product managers to deliver a modern data architecture that supports internal dashboards and future-facing operational analytics.
If you enjoy turning raw data into powerful insights and owning the full data lifecycle, this role is for you!
Responsibilities:
Take full ownership of the design and implementation of a scalable and efficient BI data infrastructure, ensuring high performance, reliability, and security.
Design and architect data products, from ingestion to transformation, modeling, storage, and access.
Build and maintain ETL/ELT pipelines, batch and real-time, to support analytics, reporting, and product integrations.
Establish and enforce best practices for data quality, lineage, observability, and governance to ensure accuracy and consistency.
Integrate modern tools and frameworks such as Airflow, Databricks, Power BI, and streaming platforms.
Collaborate cross-functional with product, engineering, and analytics teams to translate business needs into data infrastructure.
Promote a data-driven culture - be an advocate for data-driven decision-making across the company by empowering stakeholders with reliable and self-service data access.

We are looking for a strong Data Engineer to be part of one of our most strategic initiatives - building the context graph that powers our AI-SOC platform. Our team is building the context layer behind our AI Agents, transforming raw customer data into meaningful, normalized context that enables smarter autonomous security decisions.

This is a unique opportunity to join a brand-new, small, senior team with high ownership, strong product influence, and the chance to build foundational AI infrastructure from the ground up.

What Youll Be Doing:
Think like a product person - collaborate with stakeholders and consumers to design meaningful data models and continuously validate them against real customer data.
Run POCs work end to end - connect a new data source, ingest and normalize it, correlate entities across systems, and demonstrate the value.
Do the analysis that shapes the model - profile new data, measure coverage and accuracy, and find the gaps that matter to the product.
Integrate identity, endpoint, and security data sources - learn how each system exposes its data and turn it into reliable, consistent models.
Build and own scalable data pipelines, transforming raw data into clean, normalized, and well-tested models using Python, SQL, and dbt.

We are looking for a Senior Data Engineer with a strong Backend Engineering background to architect the intelligent data foundation.

This is not a traditional ETL role. You will build the critical infrastructure that feeds our Agentic Growth Engine, enabling AI agents to perceive the market, make decisions, and drive growth. You will own the semantic layer, design the agentic data architecture (both data retrieval and training infrastructure), and build sophisticated extraction processes that allow our system to "read" the competitive landscape.

What you'll do
Architect the Agentic Data Infrastructure: Design and maintain the core data pipelines and storage systems (GCP/BigQuery/Postgres) that power our AI agents, ensuring high availability and low latency for decision-making.
Build the RAG & ML Backbone: manage the infrastructure for training data, vector search, and regression testing. You will ensure our agents have access to clean, context-aware data for RAG workflows.
Develop Agentic Extraction Processes: Build complex, resilient data extraction systems (crawlers/scrapers) to map competitive landscapes and product trends, feeding raw market data into our analysis engine.
Own the ELT & Semantic Layer: Manage the transformation of raw data into a consistent, business-ready semantic layer that serves as the "source of truth" for both analytics and AI models.
Run Predictive Models: Operationalize and deploy predictive models on top of our data, integrating them into the core product workflow.Define Data Standards: As a senior owner, you will establish the data engineering best practices, coding standards, and architectural patterns that the rest of the engineering team will follow.

Were looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities-from LLMs and agents to product data insights-are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.
Key responsibilities:
Lead a team of data scientists, data engineers and product analysts
Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation
Drive excellence, code quality, and best development practices
Provide technical direction and hands-on guidance for complex AI systems
Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences
Ensure AI solutions are safe, scalable, observable, and continuously improving
Lead initiatives around product data analysis and experimentation
Analyze user interactions with product features to improve accuracy, UX, and business impact
Partner with product teams to define metrics, dashboards, and experiments that guide product improvements
Design system architectures for AI-enabled applications at scale
Evaluate and select technologies for AI platforms and data pipelines
Guide the development of prompt engineering frameworks and centralized prompt management
Ensure robust monitoring, evaluation, and feedback loops for AI outputs
Translate product and business goals into technical roadmaps and execution plans
Drive alignment between AI capabilities and measurable product outcomes.

You will manage the AI Platform Engineer(s), set the technical standards for the AI Power User group's citizen development program, and serve as the connective tissue between business leadership, platform owners, and development teams. You will shape the multi-year AI architecture roadmap while also rolling up your sleeves to conduct architecture reviews, resolve blockers, and move use cases from concept to production. This is a role for someone who can think big and execute - and who understands that in an enterprise context, the quality of your governance is inseparable from the quality of your architecture.

What You'll Own
Strategy & Architecture
Define and own the enterprise AI integration strategy - identifying opportunities to embed intelligent automation, agentic workflows, predictive analytics, and generative AI capabilities across our company core platforms
Develop and maintain reference architectures, design patterns, and the AI architecture decision log that governs how AI models connect to enterprise systems and what they are permitted to do
Consult on enterprise system architecture and implement best practices for the Enterprise Business Systems team to leverage in their day-to-day execution.
Lead Proof-of-Concept initiatives for new AI tools and platform-native AI features, evaluating them against build-vs-buy criteria before recommending adoption
Partner with business stakeholders to translate operational pain points into AI use cases with clear ROI framing and sequencing criteria
Contribute to our enterprise data strategy, ensuring AI initiatives are supported by clean, accessible, and well-governed data pipelines
Integration Architecture & Delivery

Design and own the Workato eMCP layer - the MCP governance model, persona-scoped token framework, workspace isolation strategy, and the single sanctioned action surface through which all AI agents write back to enterprise systems
Define integration patterns and standards for AI model connectivity (Claude, ChatGPT) to Salesforce, NetSuite, HiBob, and Jira - specifying what agents can read, what they can write, through which surfaces, and with what confirmation and audit requirements
Design and oversee API strategies, event-driven architectures, and middleware patterns that support scalable AI feature delivery - including agentic workflows, intelligent data transformation, anomaly detection, and natural language interfaces layered onto ERP and CRM data
Collaborate with Engineering during build phases, conducting architecture reviews, providing hands-on guidance, and resolving complex technical blockers
Define non-functional requirements - latency, security, auditability, model drift monitoring - for AI components embedded in mission-critical business processes
Establish MLOps and LLMOps practices appropriate for our enterprise environment: model versioning, observability, and rollback procedures for production AI workloads

MA?LYS is a fast-growing global body care brand, built on a proprietary e-commerce platform, deep consumer insights, and a relentless focus on performance. Millions of women worldwide use our products, and we are on a mission to become one of the most influential body care brands globally.
We operate at extreme velocity, with high standards and an obsession with results. We believe in challenging conventional thinking, making data-driven decisions, and moving quickly from idea to execution.
We are looking for a Head of AI to build and lead MA?LYS' AI transformation journey, serving as a rare opportunity for a highly strategic and deeply hands-on builder who thrives at the intersection of business, technology, and execution. In this role, you will have the ultimate playground to shape how our entire company operates, influence every major function, and create a lasting competitive advantage for a leading body care brand. Initially operating as an individual contributor partnering closely with the CEO and the Management Team. Over time, you will transition to build and lead a small, high-performing team as AI becomes an increasingly critical capability across our global e-commerce and retail operations.
To thrive in our high-pace environment, you must think like an owner, not a consultant, and be equally comfortable discussing macro business strategy with executives as well as writing code to solve a messy problem yourself. You are someone who embraces ambiguity, enjoys building from scratch, and is completely obsessed with execution. You possess the high emotional intelligence and empathy required to collaborate with non-technical stakeholders and build trust, yet you maintain the thick skin, resilience, and great sense of humor needed to handle direct communication and rapid structural pivots. Most importantly, you maintain an uncompromising commitment to quality and process improvement, ensuring that every automation or workflow you deploy actively drives our core business metrics forward without requiring hand-holding or constant reminders.
Your life at MA?LYS will look like…
* Define and lead the company's AI strategy and execution roadmap.
* Identify the highest-impact opportunities for AI across all business functions.
* Prioritize initiatives based on measurable business value, speed of implementation, and scalability.
* Design, build, deploy, and continuously improve AI-powered workflows, tools, automations, and agents.
* Translate business challenges into practical AI solutions that improve performance, efficiency, and decision-making.
* Develop production-ready applications and workflows using modern AI technologies, APIs, agents, and automation frameworks.
* Drive adoption and behavioral change across teams.
* Partner directly with business leaders to identify opportunities and implement solutions.
* Evaluate emerging technologies, models, vendors, and platforms.
* Establish governance, standards, architecture, and best practices for AI development.
* Define and track success metrics for every AI initiative, ensuring measurable impact on growth, productivity, speed, efficiency, or cost.
You will thrive in this role if you have…
* Strong technical foundation in Computer Science, Software Engineering, Data Science, or a related field.
* 5+ years of experience building technology, data, AI, automation, or software solutions in high-performance environments.
* Proven experience identifying business opportunities and translating them into deployed AI products, agents, automations, or workflows that drive measurable business impact.
* Hands-on experience working with modern generative AI technologies, LLMs, agent frameworks, APIs, embeddings, automation platforms, and production deployments.
* Strong proficiency in Python, SQL, APIs, and data-driven application development.
* Deep understanding of business operations, growth metrics, p

As an AI Engineer at our company, you will own the intelligent decision-making pipelines that turn complex workspace telemetry into autonomous security actions. You will design, build, and deploy the autonomous reasoning workflows and advanced data classification systems that drive our company's preventive operating model. Your focus will be on creating resilient, production-grade AI systems capable of deep policy comprehension, real-time prevention at the point of adoption, and autonomous remediation of existing risks.
This is a ground-floor opportunity to shape the AI strategy of a fast-growing cybersecurity company alongside a lean, elite team of builders.
WHAT YOULL DO
End-to-End Ownership: Own our AI capabilities entirely from initial research, architectural design, and prototyping, through to production deployment, optimization, and continuous monitoring.
Design & Build Agentic Workflows: Architect multi-step AI agents capable of autonomously investigating workspace risks, interpreting complex enterprise policies, and taking precise remediation actions.
Integrate Multi-Faceted ML: Bring innovation and creative thinking to our core engine. Implement diverse ML models across our entire research and product pipeline-utilizing clustering, text extraction, document analysis, and tabular data classification.
Ship Production-Grade AI: Build high-throughput, resilient, and fault-tolerant production code. You will ensure our AI pipelines and agentic workflows are highly predictable, deeply observable, and built to scale under enterprise-grade loads.
Implement Guardrails & Evaluation: Build continuous evaluation frameworks to benchmark agent accuracy, mitigate hallucinations, and enforce strict data security/privacy guardrails.

Were looking for a hands-on AI Engineer to build and own the agentic workflows at the heart of our security intelligence engine.
This is a backend-focused engineering role: youll design, build, and maintain the autonomous systems that execute security workflows end-to-end - taking capabilities from research prototype to scalable, production-grade infrastructure.
Youll be working at the edge of applied AI, turning LLMs and agentic frameworks into reliable, observable systems that operate in real customer environments.
What Youll Build:
Agentic Workflows (Core Focus): Design and maintain the orchestration backbone for multi-step, autonomous agents that investigate, reason about, and act on complex security operations.
Internal AI Infrastructure: Contribute to a shared platform for models, data pipelines, training, evaluation, and observability that the whole AI team builds on.
Responsibilities:
Build and maintain the agentic workflow engine - orchestration, tool use, state management, retries, and evaluation - that powers our AI-driven security features.
Develop the backend services and APIs that deploy AI capabilities safely and at scale.
Collaborate with AI Researchers to translate findings into production-grade autonomous workflows.
Own reliability, observability, and performance of the agentic systems in production.