SWE Manager, Fraud Science Data Platform at PayPal

Dublin, Leinster, Ireland -

Full Time

Start Date

Immediate

Expiry Date

07 Apr, 26

Salary

0.0

Posted On

07 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Software Development, Project Management, Data Engineering, BigQuery, SQL Optimization, Data Modeling, Cloud Orchestration, Python, Feature Engineering, Data Transformation, Team Leadership, Communication, Fraud Detection, Machine Learning, Data Quality, Observability

Industry

Software Development

Description

Directly manages software development projects (including program management) and execution through individual contributors. May also lead project teams across platforms or groups Implements processes to drive strong operational hygiene for all components and systems within their group Responsible for the delivery of projects, including quality and timeliness, that impact their domain and potentially one other Demonstrates strong tactical ability by managing the roadmap for a scrum team responsible for technical issues of diverse scope where analysis requires an understanding of current business or tends Competent at communicating technical issues with non-technical audiences Analyzes multiple sources of information and identifies & resolves complex technical, operational, and organizational problems relating to software development. Collaborates with direct team, managers in org, stakeholders such as Product Owners & PMO Lead and develop a team of 3-5 data platform engineers, providing technical guidance, mentorship, and coordination to align efforts across the datamart initiative. Facilitate communication and collaboration among highly skilled team members. Establish team engineering practices including CI/CD for SQL repositories, code governance, naming conventions, dependency-aware DAGs, testable transformations, and documentation standards that enable your team to work efficiently and maintain quality. Architect and build a next-generation datamart that consolidates diverse data sources into a unified, governed platform serving fraud detection, investigations, and ML modeling teams—transforming crowd-sourced investigator signals and cross-account relationships into production-grade risk detection assets. Design and implement scalable feature engineering pipelines using dbt, Dataform, or similar frameworks that translate investigative heuristics, typology insights, and linking intelligence into ML-ready features with rapid deployment cycles. Build and operate robust data transformation infrastructure across account, transaction, and session/login domains using BigQuery to support comprehensive detection of fraud, money laundering, and emerging global threats. Create semantic modeling frameworks and contribution layers that empower investigators and analysts to define detection logic without requiring deep engineering intervention. 5+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. 8+ years of production data engineering experience building and maintaining large-scale data pipelines with deep expertise in SQL optimization, data modeling, and multi-source data transformation design. 2+ years in a technical lead or management role balancing hands-on engineering with team coordination. BigQuery Mastery + Cloud Orchestration: Expert in BigQuery SQL, scripting, dynamic query execution, clustering/partitioning, and performance tuning on billion-row datasets. Hands-on experience with Airflow/Cloud Composer, dbt, or Dataform for DAG scheduling, backfills, dependency management, and production job orchestration. Advanced Data Modeling & Semantic Abstraction: Strong understanding of dimensional modeling, entity linking, identity resolution, and feature registries. Proven ability to design semantic layers that expose intuitive business logic while enforcing engineering rigor. Data Quality, Observability & Cost Governance: Experience building validation frameworks (Great Expectations, dbt tests, or custom SQL checks). Skilled in BigQuery query optimization, slot management, and designing cost-efficient large-scale pipelines. Software Engineering Excellence: Strong fundamentals in Git workflows, code reviews, testing strategies, CI/CD, operational monitoring, and incident management. Demonstrated ability to implement code standards, modular patterns, and reproducible pipeline templates. Feature Store & ML Platform Experience: Experience building or contributing to feature stores serving batch and real-time ML systems. Familiarity with feature drift, leakage control, and temporal-based testing strategies. Python for Data Engineering: Strong proficiency using Python for data processing, orchestration, and feature engineering. Team Leadership & Communication: Experience managing or leading small technical teams (3-5 engineers). Ability to facilitate collaboration among diverse working styles, mentor engineers on technical skills, and coordinate work across multiple contributors. Strong written and verbal communication skills for explaining technical decisions to stakeholders. ML Feature Engineering for Fraud/Financial Crimes: Experience translating investigative heuristics or patterns into stable, leakage-resistant ML features. Ability to collaborate with data scientists and investigators to convert domain expertise into engineered data products. Fraud/Financial Crimes Domain: Understanding of fraud typologies, AML red flags, behavioral patterns, or investigative workflows. Experience partnering directly with investigators, typology subject matter experts, or threat intelligence teams. (Note: Domain expertise is valuable but not required—we can teach fraud domain knowledge to strong engineers.) Real-time/Streaming Architectures: Exposure to near-real-time model feature serving, streaming data pipelines, or event-driven architectures beyond batch processing. Technical Leadership in Regulated Environments: Experience building auditable, governed data systems or leading technical initiatives in financial services industry. Understanding of data governance, audit requirements, and compliance considerations in platform design. Ability to describe production systems you've architected or operated at scale

Responsibilities

Manage software development projects and lead a team of data platform engineers. Architect and build a datamart for fraud detection and implement engineering practices for efficient data processing.