Lendbuzz
Finance / FinServ
DataEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Engineer at Lendbuzz. Skills: ELT data pipelines, Data Modeling, Semantic Layer, Data Quality, Self-Service Analytics. Design & Build Robust Pipelines. Develop, deploy, and maintain scalable, highly reliable, and idempotent ELT data pipelines using Python and orchestration tools like Airflow”
What You'll Achieve.
establish a "single source of truth" for business metrics; build trust with downstream consumers; empower business stakeholders, analysts, and developers to easily and independently query organizational data
Industry & Context.
critical thinking; translate complex business requirements into efficient technical architectures
What They're Looking For.
Must Have
Bachelor’s degree in CS or other relevant field, 3+ years of proven experience as a Data Engineer, Analytics Engineer, or similar role, proficiency in Python, particularly for data processing and pipeline orchestration, 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, 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 You'll Do.
Design & Build Robust Pipelines
and maintain scalable
and idempotent ELT data pipelines using Python and orchestration tools like Airflow
Lead data transformation and modeling efforts within our cloud data warehouse (e. g.
ensuring adherence to modern analytics engineering best practices (modularity
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
and monitoring to ensure data integrity and build trust with downstream consumers
Enable Self-Service Analytics
long-term data infrastructure solutions that empower business stakeholders
and developers to easily and independently query organizational data
How You'll Work.
Team & Collaboration
Cross-Functional Collaboration; Partner closely with Backend developers, BI analysts, architects, and business decision-makers to translate complex business requirements into efficient technical architectures
Communication Scope
written and verbal skills in Technical English
Process & Methodology
working and delivering end-to-end projects independently
Full Job Description
## Description 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. ## 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 Warehous
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