Software Engineering Manager - Strategic Data Solutions.

Apple's Strategic Data Solutions (SDS) team is seeking a hands-on, self-motivated Software Engineering Manager to deliver cutting-edge analytical solutions with enhanced user experience. Our mission is Enterprise Decision Automation, protecting Apple and our customers from fraud, waste, and abuse. Your contributions will be pivotal to our success.

Do you thrive on solving complex challenges with direct, meaningful impact? Are you passionate about building tools and systems that enable data analysis over petabytes of data?

Do you want to work alongside talented Data Scientists, Machine Learning Engineers, Software Engineers, Program Managers, and Apple business partners? If so, we'd love to have you on our team!

Description

With the expansive data we have, our job is to build meaningful tools and solutions that empower our internal partners and drive fraud prevention across Apple. The Solutions SWE team is a global, cross-LOB (line-of-business) engineering team focused on four core areas: efficiency solutions, internal tooling, operational excellence, and direct LOB support.

As the Engineering Manager for Solutions SWE, you will directly lead and grow a team of 5-6 Engineers based in Cork, Ireland (EMEA), while contributing to the broader functional roadmap across regions. You will balance hands-on technical leadership with people management, developing talent, and partnering with fraud investigators, analysts, engineers, product managers, and business partners to ship solutions that solve real business problems.

Minimum Qualifications

3+ years leading software engineers, either as a people manager or as a technical lead who directed the day-to-day work of a team of at least 3 engineers
Hands-on experience writing and shipping production software in Python, Java, or a comparable language, with code contributions in the last 2 years
Experience owning delivery end to end, from planning and development through deployment, monitoring and iteration
Experience working with non-engineering partners (for example product, operations, analysts or business stakeholders) to gather requirements and agree on priorities
Experience working with teams spread across multiple locations or time zones

Preferred Qualifications

Experience with or exposure to machine learning, useful for partnering with MLEs
Track record of quickly getting productive with new AI/LLM tooling.
Hands-on experience with cloud infrastructure and container orchestration (AWS, Kubernetes)
Experience with data pipeline, warehousing, and/or observability tooling (Airflow, Snowflake, Splunk, Superset)
Expertise in real-time systems, modern architecture, and data processing at scale
Experience building systems for fraud prevention or similarly challenging problems
Educational background in Computer Science or related field, or equivalent practical experience