{"id":40960,"url":"https://alion.io/job/signalfire-data-engineer-vc-backed-startups","title":"Data Engineer - VC Backed Startups","company":{"id":8867,"name":"SignalFire","domain":"signalfire.com","url":"https://alion.io/company/signalfire","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":93000,"max_usd":171000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":100},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"DynamoDB","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fivetran","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"Go","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Great Expectations","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-08-05T07:52:01Z","employer_posted_date":"2026-08-05","last_verified_at":"2026-10-21T04:59:02Z","board_verified":true,"closed_at":null,"days_open":77,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":77},"description":"Join SignalFire’s Talent Network for Data Engineer Roles at VC-Backed Startups\nThis 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 .\nAt 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.\nWe’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.\nBy 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.\nWho Should Join?\nWe’re looking for engineers who are:\nPassionate about building reliable, scalable data systems and infrastructure\nExperienced in transforming complex datasets into trusted, accessible data products\nExcited to establish data foundations in fast-moving startup environments\nComfortable partnering with engineering, product, analytics, and machine learning teams\nInterested in improving how data is collected, modeled, governed, and used across an organization\nTypical Roles & Responsibilities\nDesign, build, and maintain scalable batch and real-time data pipelines\n\nDevelop reliable data models, transformation workflows, and shared datasets for analytics and operational use cases\n\nBuild and manage cloud-based data warehouses, lakehouses, and data platforms\n\nIntegrate data from product, customer, financial, and third-party systems\n\nEstablish standards for data quality, testing, lineage, observability, and documentation\n\nPartner with analytics, product, engineering, and business teams to understand data requirements\n\nSupport machine learning and AI applications by developing dependable training, feature, and inference data pipelines\n\nImprove the performance, scalability, and cost efficiency of data infrastructure\n\nBuild self-service tools and frameworks that make data easier to discover and use\n\nImplement appropriate access controls, privacy safeguards, and data-governance practices\n\nTroubleshoot pipeline failures, data-quality issues, and performance bottlenecks\n\nHelp define the company’s broader data architecture and technical roadmap\n\nCommon Qualifications\nWhile each startup has its own hiring criteria, many Data Engineer roles in our network look for:\n3+ years of experience in data engineering, software engineering, analytics engineering, or a related technical role\n\nStrong programming skills in Python, Java, Scala, or a similar language\n\nAdvanced proficiency in SQL and experience designing scalable data models\n\nExperience building and maintaining production ETL or ELT pipelines\n\nFamiliarity with cloud platforms such as AWS, GCP, or Azure\n\nExperience with modern data warehouses or lakehouse platforms such as Snowflake, BigQuery, Redshift, or Databricks\n\nKnowledge of workflow orchestration, transformation, and data-quality tooling\n\nUnderstanding of distributed systems, data storage formats, and batch or streaming architectures\n\nAbility to collaborate with technical and non-technical stakeholders to translate business needs into data solutions\n\nStrong judgment around reliability, scalability, governance, and infrastructure tradeoffs\n\nExperience in venture-backed startups or rapidly scaling technology companies may be preferred\n\n Technologies You Might Work With:\nLanguages: Python, SQL, Java, Scala, Go\n\nWarehouses & Lakehouses: Snowflake, BigQuery, Redshift, Databricks, Delta Lake\n\nPipelines & Transformation: Airflow, Dagster, Prefect, dbt, Fivetran, Airbyte\n\nStreaming & Processing: Kafka, Spark, Flink, Kinesis, Pub/Sub\n\nCloud & Infrastructure: AWS, GCP, Azure, Docker, Kubernetes, Terraform\n\nData Quality & Observability: Great Expectations, Monte Carlo, Soda, DataHub, OpenLineage\n\nDatabases & Storage: PostgreSQL, MySQL, DynamoDB, MongoDB, S3\n\nWhat Happens Next?\nSubmit your application to join SignalFire’s Talent Ecosystem.\n\nWe review applications on an ongoing basis to identify strong candidates.\n\nIf there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.\n\nNo match yet? 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