Data Engineer

At Lendbuzz, we believe financial opportunity should be more personalized and fair. We develop innovative technologies that provide underserved and overlooked borrowers with better access to credit. From our employees to our dealers, partners, and borrowers, we’ve built a company and a culture around a resolute belief in the promise and power of diversity. We value independent and critical thinking.

We are looking for an experienced and passionate Data Engineer to join the Data Engineering Team in our rapidly growing TLV R&D site!

You will be instrumental in maintaining current pipelines and expanding our data semantic layer to support both traditional analytics and our future AI/ML initiatives.

Responsibilities include working alongside developers from the BI and Backend teams, architects and business decision makers in order to implement data pipelines and improve data architecture and infrastructure.

The Data Engineering Team in Lendbuzz focuses on building long term, scalable self-service solutions for the organizational growing data needs.

What You'll Do::
•
Design & Build Robust Pipelines: Develop, deploy, and maintain scalable, highly reliable, and idempotent ELT data pipelines using Python and orchestration tools like Airflow.

•
Own the Data Model: Lead data transformation and modeling efforts within our cloud data warehouse (e.g., Snowflake, AWS) using dbt, ensuring adherence to modern analytics engineering best practices (modularity, DRY principles, and clear separation of staging and data marts).

•
Expand the Semantic Layer: Architect and grow our centralized semantic layer to establish a "single source of truth" for business metrics, powering both traditional BI dashboards and upcoming AI initiatives.

•
Champion Data Quality & Reliability: Implement rigorous data validation, testing, and monitoring to ensure data integrity and build trust with downstream consumers.

•
Enable Self-Service Analytics: Design intuitive, long-term data infrastructure solutions that empower business stakeholders, analysts, and developers to easily and independently query organizational data.

•
Cross-Functional Collaboration: Partner closely with Backend developers, BI analysts, architects, and business decision-makers to translate complex business requirements into efficient technical architectures.

Requirements::
•
Bachelor’s degree in CS or other relevant field.

•
3+ years of proven experience as a Data Engineer, Analytics Engineer, or similar role.

•
Strong proficiency in Python, particularly for data processing and pipeline orchestration.

•
Experience in Data Modeling using dbt or equivalent.

•
Experience with Data Warehouse technologies like Snowflake, BigQuery, Redshift ,etc.

•
Experience with Orchestration platforms like Airflow, Luigi, Dagster, etc.

•
Experience with Semantic Data Layer technologies like MetricFlow, Cube or others.

•
Experience in working and delivering end-to-end projects independently.

•
Experience with at least one cloud provider, preferably AWS.

•
Strong written and verbal skills in Technical English.

Nice-to-Have::
•
Experience with ELT platforms like dlt, Fivetran, Airbyte, etc.

•
Experience with Data Validation and Testing using dbt, Great Expectations or others.

•
Familiarity with DB internals, design considerations and management.

•
Familiarity with containerized deployments with K8s.

•
Familiarity with Event Streaming platforms like Kafka, Redpanda, etc.

What we offer:
- A culture that values product ownership, collaborative architectural planning, and building wins for your resume/portfolio as much as for the company.
- Smart, dynamic people with whom you can share the experience of building something unique.
- Competitive salary with opportunities for growth and advancement.

A Note on Recruiting Outreach
We’ve been made aware of individuals falsely claiming to represent Lendbuzz using lookalike email addresses (eg @lendbuzzcareers.com). Please note that all legitimate emails from our team come from @lendbuzz.com. We will never ask for sensitive information or conduct interviews via messaging apps.