Lead Data Engineer

The Risk & AI team within Customer Success is highly engaged with our clients and solves technical and analytical problems with a business-focused mentality and customer-facing attitude. They activate, maintain, and support clients which includes designing risk management strategies, developing models and rules, implementing data pipelines to make them successful, and providing client enablement training. Additionally, they work cross-functionally with other departments such as research, product, marketing with a collaborative team spirit spanning the globe to ensure Feedzai delivers best-in-class risk prevention solutions. Being on the frontline of fighting crime and protecting people from financial harm is incredibly inspiring to each of us. Join Us!

Your Day-to-Day:

• Understand, profile, and assess the quality of raw data from diverse client sources
• Design, build, test and maintain scalable and robust data pipelines for ingestion, transformation, and enrichment
• Implement automated data validation, cleaning, and quality assurance processes
• Collaborate with data scientists to support feature engineering and data preparation workflows
• Optimize performance and cost efficiency of data workflows across cloud and on-prem environments
• Lead discussions with client stakeholders on data architecture, integration, and delivery strategies
• Partner with internal teams (Product, Engineering, Research) to improve platform tooling and reusability
• Mentor and guide junior engineers in best practices, architecture design, and coding standards

You Have & You Know-how:

• Bsc or MSc or PhD in computer science, software engineering, or related technical field
• Expertise in distributed data processing frameworks (Spark, Hadoop, or similar)
• Strong coding skills in Python or Go or Scala; proficiency in python.
• Experience with workflow orchestration tools (e.g., Airflow, Dagster, Prefect)
• Deep understanding of data modeling, warehousing, and ETL best practices
• Familiarity with resource optimization and monitoring at both system and application levels
• Familiarity with modern data stack tools and warehouse technologies (e.g., dbt, Snowflake, BigQuery, Redshift)
• Experience working in cloud environments (AWS, GCP, or Azure) with infrastructure-as-code tools 
• Bonus: Experience with ML data pipelines and feature stores
• Strong communication skills to articulate technical concepts to diverse audiences

The Customer Success Team is responsible for delivering our product to our clients. This includes education, configuration, solution development, and risk strategy to enable our clients to address their pain points. We collaborate with our clients to ensure they have the right solution, build out a strategy and training plan for them, and then support them through each phase of our client lifecycle. We grow at a fast clip and believe no challenge is too big or too small. Therefore, we have an open environment that encourages us to lean in, try new things, and discover our potential. Join Us!