Forward-Deployed Engineer - Global Sourcing & Supply Management (GSSM) Solutions

Apple•Published 1 days ago•First seen 1 days ago

Summary

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

We’re looking for customer-facing Forward-Deployed Engineers (FDEs) who can rapidly identify operational pain points, build practical AI-powered solutions and deploy them in real-world environments.

Description

This role sits at the intersection of Engineering, Product, Data and Customer Success. You’ll work directly with users to understand workflows, uncover the highest-impact problems and quickly deliver solutions using AI, automation, APIs, data engineering, ETL/ELT pipelines and cloud tooling.

Success in this role requires strong engineering ability, data engineering fundamentals, operational problem-solving, communication skills and the ability to move from ambiguity to implementation quickly. You are not just advising on solutions - you are building and operationalising them.

Responsibilities

Partner directly with business stakeholders, engineers and product managers to understand operational workflows, deployment requirements and process bottlenecks
Translate ambiguous business needs into practical, scalable technical solutions
Identify high-impact operational pain points and rapidly deliver solutions that create measurable value
Scope, design, build and deploy AI-powered internal tools, workflow automations and system integrations across test and production environments
Lead business implementations and deployments as a Forward-Deployed Engineer / Technical Product Manager (FDE/TPM)
Build and maintain integrations across APIs, systems and data platforms
Design and implement data pipelines and ETL/ELT workflows to ingest, clean, transform, validate and prepare data for AI and data-intensive applications
Work with large, complex and heterogeneous datasets across multiple enterprise systems and data sources
Develop data-processing workflows that combine structured and unstructured data, including databases, spreadsheets, documents, APIs and other enterprise data sources
Establish data quality checks, validation processes and monitoring to ensure data is reliable and usable by downstream applications
Troubleshoot data pipelines and investigate issues related to data quality, schema changes, ingestion failures, transformation errors and data availability
Work with engineering teams to design data architectures and pipelines that support scalable AI applications, analytics and automation workflows
Troubleshoot production issues, debug live environments and drive issues through resolution
Guide user adoption, training and iterative improvement as workflows and AI capabilities evolve
Partner closely with product and engineering teams to improve platform capabilities based on real-world operational feedback
Operate effectively in dynamic environments with evolving business and deployment requirements

Minimum Qualifications

Bachelor's or Master's degree in Computer Science, Software Engineering, Electrical Engineering or a related technical field
4-7 years of experience in software engineering, solutions engineering, technical consulting, data engineering or other business-facing technical roles
Strong hands-on engineering skills with Python, APIs, infrastructure platforms and systems integrations
Strong understanding of data engineering concepts, including data ingestion, cleaning, transformation, ETL/ELT, data validation and pipeline design
Hands-on experience building or maintaining data pipelines for data-intensive applications
Experience working with structured and unstructured data and transforming data from multiple sources into formats suitable for downstream applications
Experience with SQL and relational databases, including querying, data transformation and troubleshooting data-related issues

Preferred Qualifications

Experience deploying, supporting and troubleshooting production systems in enterprise environments
Experience building integrations and automating workflows across multiple systems
Experience working with APIs, databases, files and other data sources to build reliable data ingestion and processing workflows
Practical experience using LLMs or GenAI APIs to create reliable workflows, automations or internal tools
Familiarity with modern AI tooling, agent frameworks, prompt engineering and orchestration platforms
Understanding of the data requirements and challenges involved in building AI/ML applications, including data preparation, retrieval, evaluation and data quality
Strong problem-solving skills and comfort operating in ambiguous, fast-changing environments
Demonstrated ability to independently drive solutions from requirements gathering through deployment and iteration
Proven ability to collaborate cross-functionally across engineering, product, operations, data and business teams
Japanese language proficiency, with the ability to communicate effectively with Japanese-speaking customers and stakeholders and understand technical and business requirements in Japanese.
Experience working in startup or high-growth environments with rapidly evolving priorities
Strong product intuition and business empathy, with the ability to influence product direction based on field feedback