Data Engineer - VC Backed Startups
SignalFire · Remote - No Preference ·
- Work mode
- Remote
- Category
- Data engineering
- Experience
- 3+ years
- Company size
- 51-200
Build scalable data pipelines and infrastructure for early-stage startups in AI, cybersecurity, healthtech, fintech, and devtools, enabling trusted data products and analytics.
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Data Engineering talent. If you have any questions, please direct inquiries to [email protected].
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re 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.
We’re looking for engineers who are:
✔ Passionate about building reliable, scalable data systems and infrastructure
✔ Experienced in transforming complex datasets into trusted, accessible data products
✔ Excited to establish data foundations in fast-moving startup environments
✔ Comfortable partnering with engineering, product, analytics, and machine learning teams
✔ Interested in improving how data is collected, modeled, governed, and used across an organization
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
While each startup has its own hiring criteria, many Data Engineer roles in our network look for:
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
Experience in venture-backed startups or rapidly scaling technology companies may be preferred
Languages: Python, SQL, Java, Scala, Go
Warehouses & Lakehouses: Snowflake, BigQuery, Redshift, Databricks, Delta Lake
Pipelines & Transformation: Airflow, Dagster, Prefect, dbt, Fivetran, Airbyte
Streaming & Processing: Kafka, Spark, Flink, Kinesis, Pub/Sub
Cloud & Infrastructure: AWS, GCP, Azure, Docker, Kubernetes, Terraform
Data Quality & Observability: Great Expectations, Monte Carlo, Soda, DataHub, OpenLineage
Databases & Storage: PostgreSQL, MySQL, DynamoDB, MongoDB, S3
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future Data Engineering roles across our portfolio.