Data Engineer - VC Backed Startups

Department: EPD

Location: Remote - No Preference

Employment Type: FullTime

Join SignalFire’s Talent Network for Data Engineer Roles at VC-Backed Startups

🛑 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 talentnetwork@signalfire.com.

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.

Who Should Join?

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

Typical Roles & 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

Common Qualifications

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

💡 Technologies You Might Work With:

• 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

What Happens Next?

1. Submit your application to join SignalFire’s Talent Ecosystem.

2. We review applications on an ongoing basis to identify strong candidates.

3. If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.

4. No match yet? We’ll keep your profile on file for future Data Engineering roles across our portfolio.