Lead Data Engineer

Summary

Join Feedzai's Risk & AI team within Customer Success and contribute to the world's first RiskOps platform. You will be responsible for understanding and assessing data quality, designing and maintaining data pipelines, implementing data validation processes, collaborating with data scientists, optimizing data workflows, and leading discussions with clients. The role also involves partnering with internal teams to improve platform tooling and mentoring junior engineers. This position requires expertise in distributed data processing frameworks, strong coding skills in Python or similar languages, and experience with workflow orchestration tools. The ideal candidate will also possess a deep understanding of data modeling and warehousing best practices. Feedzai offers a collaborative and supportive environment with opportunities for professional growth.

Requirements

• 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
• Strong communication skills to articulate technical concepts to diverse audiences

Responsibilities

• 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

Preferred Qualifications

Experience with ML data pipelines and feature stores