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Quandri
Quandri
51 – 200 Employees
Artificial IntelligenceFintechInsurance
Quandri is a technology company serving the insurance brokerage sector with its Renewal Intelligence Platform. Built for personal lines brokerages, the platform uses artificial intelligence to automate repetitive renewal work, surface actionable client insights, and help brokers manage their books more efficiently. Quandri’s focus is on improving renewal workflows, supporting client retention, and giving brokerage teams better visibility into the data behind their operations. The company works with major Canadian brokerages and combines insurance expertise with AI-driven software development.
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Senior Data Engineer at Quandri (Vancouver Hybrid)

Lead Quandri’s Databricks data lake, production pipelines, and AI data infrastructure. Build the systems powering its AI platform for insurance agencies and brokerages.

CACanada
$140,000 – $170,000 / year
HybridFull-timeSeniorEnglish RequiredData Engineer
AirflowApacheAWSAzureCloudDynamoDBGoogle Cloud PlatformPostgresPythonSQLTableau

Description

  • Lead the full Databricks data lake lifecycle, including dbt models, medallion layers, incremental processing, backfills, partitioning, freshness, and quality monitoring.
  • Implement CDC and streaming ingestion from HubSpot, Langfuse, Postgres, and DynamoDB, with reliable idempotency and deduplication.
  • Own data services, schema and migration planning, versioned APIs, provenance, audit trails, testing, and observability.
  • Develop AI data foundations such as embedding pipelines, vector stores, retrieval knowledge bases, feature stores, and LLM observability.
  • Build and maintain cloud databases in partnership with the Infrastructure team.
  • Enhance data retrieval and improve the performance and usability of analytics dashboards.
  • Maintain policies governing data management and security.
  • Partner with software, AI/ML, and data engineers, data scientists, product teams, and business units to align technical requirements.
  • Explain technical concepts effectively to stakeholders without technical backgrounds.
  • Coach engineers on sound data engineering practices.

Requirements

  • Bring 4–6 years of professional experience in data engineering.
  • Have a track record of building and maintaining databases, data pipelines, and backend systems, with emphasis on efficient data management and integrated system components.
  • Work proficiently in Python, preferred, and SQL.
  • Have designed multi-tenant data systems with strict isolation requirements and experience handling PII or other regulated information.
  • Understand data modeling, including medallion architecture, star schemas, or Snowflake schemas.
  • Have practical experience with cloud platforms such as AWS, Azure, or GCP.
  • Have used dbt, Databricks Workflows, Apache Airflow, Prefect, Dagster or comparable tools, change-data-capture systems, or AWS Step Functions.
  • Use data visualization platforms such as Databricks SQL dashboards, Tableau, or an equivalent Power BI capability.
  • Have experience developing or supporting AI products.
  • Understand and apply data governance practices.
  • Hold a bachelor’s or master’s degree in Computer Science, Data Engineering, Computer Engineering, or a related technical field, or offer equivalent experience; this is a bonus.

Benefits

  • Receive employee stock options determined by experience level at hire and subject to the standard vesting schedule.
  • Receive employee stock options based on experience level.
  • Access comprehensive health coverage, including a $500 Lifestyle Spending Account.
  • Take four weeks of paid vacation each year.
  • Work from anywhere in the world for up to 60 calendar days annually.
  • Receive parental-leave top-ups of six months for birthing parents and eight weeks for non-birthing parents, for salaries up to $100,000 annually.

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