Data Engineer

Superhuman Superhuman · Consumer · Hub - Berlin · Engineering, Product, Design, and Marketing

Data Engineer on the Data Enablement Engineering team responsible for building and scaling a world-class data platform that handles billions of daily events. The role involves architecting and leading the development of large-scale data pipelines, data lakes, and ensuring data availability, security, and scalability. Collaboration with ML engineers and other stakeholders is key to powering product features and data-driven decision-making.

What you'd actually do

  1. Architect and lead the development of large-scale systems for data pipelines, data lakes that handle billions of daily events.
  2. Design and implement solutions that ensure data is available, secure, and scalable across the platform, enabling real-time and batch processing.
  3. Make high-level architectural decisions about system design, technology choices, and platform evolution, ensuring scalability and long-term sustainability.
  4. Collaborate with key stakeholders, such as product teams, data engineers, back-end developers, and ML engineers, to build tools & frameworks that power analytics, product features, and data-driven workflows.
  5. Work with business stakeholders such as Analytics Eng, Data Science, Machine Learning, and Product teams to build high-impact data products enabling business-critical features, research, and experimentation with a focus on efficiency and alignment

Skills

Required

  • Experience in large-scale distributed computing systems
  • Expertise in data engineering
  • Expertise in data modeling
  • Experience managing live production environments
  • Expert in SQL
  • Proficient in Spark
  • Proficient in Kafka
  • Proficient in Terraform
  • Proficient in at least one programming language commonly used for data engineering (e.g., Python, Scala, or Java)
  • Strong knowledge of ETL/ELT design patterns
  • Strong knowledge of orchestration tools (e.g., Airflow, dbt, Dagster)
  • Strong knowledge of data quality frameworks
  • Ability to design scalable, secure, and maintainable data models and architectures
  • Understanding of data governance
  • Understanding of privacy regulations (GDPR/CCPA)
  • Hands-on experience with modern data storage technologies (e.g., Delta Lake, Snowflake, BigQuery, Redshift)
  • Strategic thinking
  • Excellent communication and collaboration skills
  • Ability to work independently with minimal to zero guidance
  • Proactively manages tasks and priorities across multiple projects
  • Analyzes and executes work efficiently
  • Collaborates effectively with cross-functional teams
  • Thrives in fast-paced, results-driven environments

Nice to have

  • Experience with AI/ML data pipelines

What the JD emphasized

  • handling billions of daily events
  • large-scale systems
  • data pipelines
  • data lakes
  • scalability
  • real-time and batch processing
  • ML engineers

Other signals

  • ingest over 60 to 70 billion daily events
  • build a world-class data platform
  • design and lead the implementation of robust, scalable, and reliable systems that handle large volumes of data