Data Foundations Engineer

Designs and scales modern data architectures powering Wallet, Payments, and Commerce products. Focuses on building high-performance data pipelines and enabling analytics and ML use cases, with strong fundamentals in data modeling and scalable systems. Requires experience with data engineering for analytics or ML systems, SQL, Python/Scala/Java, Spark, Kafka, Airflow, data modeling, lakehouse architectures, cloud platforms (AWS/Azure/GCP), Snowflake/Databricks, CI/CD, data observability, MLOps, GenAI/RAG pipelines, and LLMs. Experience in FinTech, Wallet, or Payments domain is required.

What you'd actually do

  1. Design and implement scalable batch and near-real-time data pipelines.
  2. Develop ETL/ELT workflows optimized for performance and cost.
  3. Implement dimensional data models and standardize business metrics.
  4. Instrument APIs and user journeys to capture behavioral and transactional data.
  5. Ensure data integrity, governance, privacy, and compliance.

Skills

Required

  • data engineering
  • analytics
  • ML systems
  • SQL
  • Python
  • Scala
  • Java
  • Spark
  • Kafka
  • Airflow
  • data modeling
  • lakehouse architectures
  • AWS
  • Azure
  • GCP
  • Snowflake
  • Databricks
  • CI/CD
  • data observability
  • MLOps
  • GenAI
  • RAG pipelines
  • LLMs
  • prompt engineering
  • fine-tuning
  • FinTech
  • Wallet
  • Payments

Nice to have

  • Trino
  • OLAP/NRT systems
  • Superset
  • Tableau
  • infrastructure-as-code

What the JD emphasized

  • 6+ years of experience in data engineering for analytics or ML systems.
  • Hands-on experience with LLMs (prompt engineering, fine-tuning, RAG).

Other signals

  • data pipelines
  • analytics and ML use cases
  • data modeling
  • scalable systems
  • MLOps
  • GenAI/RAG pipelines
  • LLMs