Data Engineer - VC Backed Startups

SignalFire • San Francisco, CA

5d

About The Position

Join SignalFire’s Talent Network for Data Engineer Roles at VC-Backed Startups. This is not an application for a specific job, but a way to get on the radar of VC-backed startups that are actively hiring Data Engineering talent. SignalFire partners with top early-stage startups that are shaping the future of technology across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS. We are looking to connect with exceptional Data Engineers who are excited about building scalable data infrastructure, developing reliable pipelines, and enabling teams to make better decisions with trusted data. By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.

Requirements

• 3+ years of experience in data engineering, software engineering, analytics engineering, or a related technical role
• Strong programming skills in Python, Java, Scala, or a similar language
• Advanced proficiency in SQL and experience designing scalable data models
• Experience building and maintaining production ETL or ELT pipelines
• Familiarity with cloud platforms such as AWS, GCP, or Azure
• Experience with modern data warehouses or lakehouse platforms such as Snowflake, BigQuery, Redshift, or Databricks
• Knowledge of workflow orchestration, transformation, and data-quality tooling
• Understanding of distributed systems, data storage formats, and batch or streaming architectures
• Ability to collaborate with technical and non-technical stakeholders to translate business needs into data solutions
• Strong judgment around reliability, scalability, governance, and infrastructure tradeoffs

Nice To Haves

• Experience in venture-backed startups or rapidly scaling technology companies

Responsibilities

• Design, build, and maintain scalable batch and real-time data pipelines
• Develop reliable data models, transformation workflows, and shared datasets for analytics and operational use cases
• Build and manage cloud-based data warehouses, lakehouses, and data platforms
• Integrate data from product, customer, financial, and third-party systems
• Establish standards for data quality, testing, lineage, observability, and documentation
• Partner with analytics, product, engineering, and business teams to understand data requirements
• Support machine learning and AI applications by developing dependable training, feature, and inference data pipelines
• Improve the performance, scalability, and cost efficiency of data infrastructure
• Build self-service tools and frameworks that make data easier to discover and use
• Implement appropriate access controls, privacy safeguards, and data-governance practices
• Troubleshoot pipeline failures, data-quality issues, and performance bottlenecks
• Help define the company’s broader data architecture and technical roadmap

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed