Lead Data Engineer

Allergan Aesthetics, an AbbVie company, develops and markets a portfolio of leading aesthetics brands and products. The Lead Data Engineer will collaborate with stakeholders to solve technical challenges, oversee technical solutions, and ensure data quality and compliance while developing data products and maintaining ETL processes.

Responsibilities:

• Take ownership for achieving objectives and key results for your team, oversee & own technical solutions, communicate schedule, status, and milestones
• Collaborate with cross functional partners (Product Managers, Data Scientists, Machine Learning Engineers, Software Engineers, and Business teams) to build data products
• Communicate effectively with both technical and non-technical stakeholders. Translate technical concepts into clear, accessible terms
• Develop, optimize, and maintain complex ETL processes for data movement and transformation
• Review code and provide technical guidance to ensure adherence to high-quality standards and best practices in data engineering
• Develop APIs and microservices to expose and integrate data products with software systems
• Implement monitoring, logging, and alerting systems to proactively identify and resolve issues
• Ensure data quality, security, and compliance with relevant regulations and standards
• Stay current with industry trends, emerging technologies, and best practices in data engineering. Foster a culture of continuous learning and skill development within the team

Requirements:

• BS, MS, or PhD in Computer Science, Mathematics, Statistics, Engineering, Operations Research, or a related quantitative field
• 7+ years of experience as a Data Engineer or Software Engineer developing and maintaining data pipelines, infrastructure and architecture
• Strong programming skills in Python with a solid understanding of core computer science principles
• Knowledge of relational and dimensional data modeling for building data products
• Experience with data quality checks and data monitoring solutions
• Experience orchestrating complex workflows and data pipelines using Airflow or similar tools
• Proficiency with Git, CI/CD pipelines, Docker, and Kubernetes
• Experience architecting solutions on AWS or equivalent public cloud platforms
• Experience developing data APIs, microservices, and event-driven systems to integrate data products
• Strong interpersonal and verbal communication skills
• Proven leadership experience with the ability to mentor and guide a team
• Familiarity with data mesh concepts
• Domain knowledge in recommender systems, fraud detection, personalization, and marketing science
• Understanding of vector databases, knowledge graphs, and other advanced data organization techniques
• Hands-on experience with tools such as Snowflake, Postgres, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, SageMaker, Datadog, PagerDuty, data observability tools, and data governance tools