Senior Software Engineer, Data

November 26

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Vendelux

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤝 B2B

☁️ SaaS

💰 $2.4M Seed Round on 2022-01

Description

• As a Senior Software Engineer - Data at Vendelux, you will work on improving the quality, quantity, resilience and processing speed of our most precious asset - our data. • Develop, release, and maintain high quality data pipelines, ETL and ELT processes. • Own and guide the development of our data infrastructure. • Develop custom integrations and APIs to automate our data collection, cleaning and enrichment pipelines. • Promote a culture of collaboration and learning across engineering, product, and design team via mentoring, documentation, presentations, or other knowledge-sharing methods. • Ensure our data is always available by participating in our on-call rotation. • Work with stakeholders including data-ops, product and executives and assist them with data-related technical issues. • Experience with data pipelines and workflow management tools like Dagster or Airflow, data warehouses like Snowflake or Databricks, and Cloud-based infrastructure like AWS, Google Cloud, Azure. • Determine where in our infrastructure we should host data based on the use case and data model. • Collaborate with Data Scientists in order to design scalable implementations of their models. • Identify bottlenecks and implement improvements to our data and engineering processes, and tools.

Requirements

• Minimum of 6 years of relevant engineering experience and exposure to working with data, data pipelines and/or machine learning. • Expert level knowledge of python. • Track record of shaping and shipping valuable features. • Track record of driving high-leverage improvements to infrastructure and process. • Judgment to take on technical debt and risk where appropriate. • Strong communication skills, especially written. Our engineering team is remote-first, so a lot of important work happens in chats and documents. • Previous startup experience. • Nice-to-have: DevOps or platform/infrastructure experience (or deep interest). • Nice-to-have: experience with ML Ops processes and designing/building/deploying/productizing ML models.