Senior/Principal Data Engineer

Position Overview

A leading technology organization is seeking a Senior or Principal Engineer to lead the development of a next-generation data infrastructure initiative focused on identity intelligence. This greenfield project will serve as the backbone for a suite of advanced fraud prevention and identity verification solutions.

The ideal candidate will bring deep expertise in data architecture, graph databases, and backend engineering, along with a passion for building scalable systems that power real-time analytics and machine learning. This role offers high visibility and strategic impact, with direct support from senior leadership.

Key Responsibilities

• Architect and implement scalable, secure, and high-performance data platforms using modern cloud technologies.
• Lead and mentor a team of engineers through design reviews, code reviews, and technical guidance.
• Define and enforce best practices in data engineering, including testing, deployment, and data governance.
• Design solutions using data lakes, warehouses, and graph databases to model complex relationships.
• Build and maintain ETL pipelines for batch and streaming data to support both model training and inference.
• Develop low-latency APIs for querying graph data and generating machine learning features.
• Collaborate with legal and security teams to ensure compliance with data privacy and regulatory standards.
• Communicate technical concepts clearly to both technical and non-technical stakeholders.
• Stay current with emerging technologies in big data, graph analytics, and cloud infrastructure.
• Contribute to open-source initiatives and engage with technical communities.

Ideal Qualifications

• 10-15+ years of experience in data engineering, with at least 4 years in a leadership role.
• Proven track record of building and scaling data systems in AWS, GCP, or Azure.
• Proficiency in Python, SQL, and one or more of Java, Scala, or Go.
• Hands-on experience with streaming frameworks (e.g., Spark, Kafka, Flink).
• Deep knowledge of graph databases (e.g., Neo4j, Neptune, OrientDB) and graph algorithms.
• Strong foundation in software architecture, design patterns, and data structures.
• Experience with Docker, Kubernetes, and microservices architecture.
• Familiarity with CI/CD pipelines and DevOps practices.

Preferred Skills

• Experience building ML feature pipelines using platforms like Databricks.
• Knowledge of cybersecurity, compliance, and identity resolution.
• Expertise in GraphQL APIs, JVM performance tuning, and caching strategies.
• Background in fraud detection systems or similar high-impact domains.