Sr. Data Engineer (fintech)
Location:
Herzliya / hybrid
Our amazing client is a FinTech startup that has developed an AI-powered platform designed to identify, intercept, and reverse false payment declines in real time, ensuring legitimate transactions succeed seamlessly. As a leader in payment decline recovery, they help businesses increase revenue, capture new customers, improve customer lifetime value, strengthen brand reputation, and reduce customer acquisition costs.
About the Role
This role requires proven hands-on experience designing, building, and operating large-scale data platforms on Databricks, with the ability to extend that data infrastructure into AI/ML and LLM-powered systems.
You'll own the full data lifecycle — from ingestion and pipeline architecture to feature engineering, model-ready datasets, and inference-serving infrastructure. You'll work across the stack: building robust ETL/ELT pipelines on Databricks and AWS, standing up the data foundations that power our ML models and AI workflows, and partnering closely with engineering and product to turn data into production intelligence.
This is a full-stack data engineering role for someone equally comfortable optimizing a Spark job and integrating an LLM into a production pipeline.
Responsibilities
Design, build, and maintain scalable data pipelines and ETL/ELT workflows on Databricks (Delta Lake, Spark, Workflows/Jobs)
Architect and manage data lakehouse infrastructure across Databricks and AWS (S3, Redshift, Glue, etc.)
Build feature pipelines and model-ready datasets to support machine learning and predictive modeling
Develop and deploy predictive ML models and support experimentation pipelines
Build and integrate RAG pipelines, vector retrieval, and LLM-based automation into internal tools and data workflows
Integrate AI/ML capabilities with internal APIs, services, and data platforms
Implement data quality, observability, and monitoring practices across pipelines and AI workloads
Optimize pipeline performance, cost, and reliability at scale
Collaborate with engineering and product teams to identify where better data infrastructure and AI can improve internal tools and decision systems
Contribute to architectural decisions around the company's data and AI platform strategy
Requirements
4+ years of experience in data engineering, software engineering, or a related field, with hands-on Databricks experience (Spark, Delta Lake, Unity Catalog, Workflows)
Strong programming skills in Python and SQL
Experience designing and operating production-grade data pipelines on large datasets
Experience working with AWS (S3, Redshift, Glue, Lambda, or similar)
Solid understanding of ML fundamentals (feature engineering, model training/evaluation) as applied to data pipeline design
Familiarity with LLM APIs and modern AI frameworks, and how to integrate them into data-driven systems
Experience with data modeling, warehousing, and lakehouse architecture
Understanding of CI/CD, orchestration, and monitoring for data and ML workloads
Strong system design and problem-solving skills
Ability to take ownership end-to-end and collaborate across data, ML, and product teams
Nice to Have
Experience building LLM-based agents, RAG architectures, or AI automation systems
Familiarity with LangChain, LangGraph, or similar frameworks
Experience with vector databases and retrieval systems
Experience with distributed systems or large-scale batch/streaming data processing (Spark Structured Streaming, Kafka)
Databricks certifications (Data Engineer Associate/Professional, ML) a plus
Experience with dbt, Airflow, or similar orchestration tools
Why Join Us
Own the data foundation that powers production AI systems used across the organization
Work with modern data platforms (Databricks, AWS) and cutting-edge AI/LLM technologies
Join a highly technical, full-stack engineering team
Help shape the company's data and AI infrastructure from the ground up