Head of Data Engineering

Niuro · Remote

Get on Board Posted May 26, 2026 First seen May 31, 2026
  • Senior Data Engineer with 5+ years designing and operating production data pipelines at scale, ideally at a fintech or lending company where data became the gating constraint on growth
  • C1 English level or higher — role requires clear communication with a US-based executive team and non-technical stakeholders
  • Demonstrated ability to produce a diagnostic and roadmap within the first weeks in a new role and present it credibly to technical and non-technical audiences
  • Experience implementing medallion architecture with explicit schema contracts and lineage between layers
  • Strong proficiency in Azure (Data Factory, ADLS) and Google Cloud (BigQuery); Databricks experience (asset bundles, Unity Catalog, GitHub Actions deployment) is a strong plus
  • Track record of hiring and leading data engineering teams (2–5 engineers), including standing up intake processes and cross-functional data contracts
  • Production-level Python and strong SQL fluency including BigQuery-specific optimization patterns (partitioning, clustering, cost-control idioms)
  • Hands-on experience with orchestration frameworks: Airflow, Dagster, or Azure Data Factory pipelines
  • Comfortable operating hands-on alongside the team — this is not a purely managerial role
  • Lead Data Engineering function build-out from zero: own all pipelines and establish freshness monitoring across Azure (Data Factory, ADLS, Databricks) and Google Cloud (BigQuery, Looker), plus third-party integrations (Plaid, FactorTrust, Zendesk, AirCall, Amplitude, Epic)
  • Implement medallion architecture: bronze (raw ingestion) → silver (curated per consumer domain) → gold (feature stores + agent-ready data planes for ML models and AI workflows)
  • Build data catalog and dictionary with canonical join keys and full applicant data map; define schema governance and data contracts with upstream systems
  • Deliver a diagnostic and roadmap within the first 30 days: architecture inventory, business unit needs assessment, sequenced build plan, and resource requirements presented to executive stakeholders
  • Stand up intake and request-management process for cross-functional stakeholders: Underwriting, Collections, Marketing, Product, Data Science, Executive
  • Implement audit trails, identity governance, and cost monitoring across BigQuery and Azure; establish cross-functional contracts with Engineering and Product so application changes ship with observability intact
  • Evaluate and drive cloud consolidation strategy: current state is Azure + GCP (BigQuery/Looker); strategic direction is Azure + Databricks — propose, justify, and execute the path
  • Hire and lead the data engineering team as the function scales to +1 platform engineer + 1 analyst-engineer within months 3–9
Niuro connects projects with elite tech teams to empower partnerships with top-tier U.S. companies. Our mission is to simplify global talent acquisition through innovative solutions, enabling continuous growth and delivery excellence.
You will be the first Head of Data Engineering at a US-based fintech processing ~1,000 merchant cash advances monthly. Build the data function from zero: own all pipelines, implement medallion architecture (bronze/silver/gold), and establish governance, monitoring, and cost controls across Azure and BigQuery. This is a peer role to the Head of Engineering and Chief Data Scientist, reporting to the CEO, with a clear path to lead a small team from month three onward
You’ll join a global, remote-first environment that supports ongoing training, leadership development, and a culture of technical excellence. This role focuses on building scalable integrations that underpin complex workflows and data pipelines for our clients.
  • Prior experience at a fintech operating Plaid integrations or equivalent open banking / bank-data aggregation products
  • Experience migrating reporting layers from GCP (BigQuery/Looker) to Azure-native tooling (Power BI, Microsoft Fabric) — Fundo is actively evaluating this consolidation
  • Hands-on with Databricks Asset Bundles and GitHub Actions for automated pipeline deployment; experience with Unity Catalog and DLT is a plus
  • Experience deploying agentic or AI-assisted data workflows — semantic layers, LLM-accessible gold-layer data planes, or AI-assisted pipeline monitoring (Slack-integrated alert agents, auto-PR generation)
  • Background working with gig economy, MCA, or informal-worker financial data — familiarity with transaction-heavy, non-bureau underwriting data shapes is a meaningful accelerator
  • Spanish — most of the engineering team operates bilingually