Lead Data/Analytics Engineer

Dutch Vet

. Own the event pipeline from instrumentation through Segment into Amplitude, Iterable, and Snowflake, including sources, destinations, and reverse ETL .

Posted 9/17/2026full-timeVancouver • CanadaSenior💰 CA$185,000 - CA$220,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in analytics engineering and data engineering, with a strong focus on SQL, Python, and cloud data warehousing using Snowflake. Proficient in building and optimizing ELT pipelines, designing tracking plans, and conducting A/B tests to drive data-driven decision-making.

Highest-signal resume keywords

SQL ProficiencyPython ProficiencySnowflake ExperienceDbt Production ExperienceAmplitude Analytics

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Analytics EngineeringData EngineeringEvent Pipeline ManagementA/B TestingData Quality AssuranceTracking Plan DesignData Mart DevelopmentExperiment AnalysisData GovernanceRoot-Cause Analysis

Soft Skills

Self-DirectedPrioritizationCollaboration

Tools & Technologies

AmplitudeSegmentDbtPrefectGitHub ActionsBI ToolsCloud Infrastructure

Industry Keywords

Data WarehouseEvent TrackingProduct AnalyticsData QualitySemantic Layer

Tech Stack

Tools & technologies

AirflowAWSAzureCloudETLGoogle Cloud PlatformPythonSQLTableau

About the role

Key responsibilities & impact

Own the event pipeline from instrumentation through Segment into Amplitude, Iterable, and Snowflake, including sources, destinations, and reverse ETL
Define and publish a typed event schema and governance workflow for web and mobile teams
Design and validate tracking plans for new features and surfaces and confirm end-to-end event flow before launch
Partner with product managers to design Amplitude experiments, including hypotheses, metrics, guardrails, and test sizing
Build Amplitude charts, funnels, cohorts, and dashboards
Debug identity resolution, user properties, and experiment assignment issues
Analyze experiment results and present findings with recommendations
Build, maintain, and optimize ELT pipelines using dbt, Prefect, and Fivetran
Own core Snowflake datasets and dbt models with BI and product engineering teams
Lead infrastructure modernization, including orchestration and dbt upgrades, runtime reduction, and Snowflake cost optimization
Drive warehouse security and governance, including access controls, security remediation, and PII handling
Implement data quality tests, freshness checks, observability, and alerting
Troubleshoot pipeline failures and data incidents and drive root-cause fixes
Deliver data marts powering recommendations and personalization and their measurement baselines
Contribute to the semantic layer as a single source of truth for metrics
Supply governed data to AI features supporting member experience and vet workflows
Define and document core company metrics

Requirements

What you’ll need

6+ years of experience in analytics engineering, data engineering, or hybrid data roles
Strong proficiency in SQL and Python
Deep hands-on experience with Snowflake or a comparable cloud data warehouse
Production experience with dbt, including testing, documentation, and CI
Experience with product analytics platforms, ideally Amplitude, including funnels, cohorts, experiment configuration, and identity resolution
Experience with customer data platforms and event pipelines, ideally Segment, including tracking plan design, destination management, and debugging
Demonstrated experience designing and analyzing A/B tests or product experiments
Experience with orchestration tools such as Prefect, Airflow, or Dagster
Comfortable with Git-based workflows and CI/CD tools such as GitHub Actions
Uses AI tools (code assistants, LLMs, etc.) as part of daily work and can show how
Familiarity with BI tools like Sigma, Looker, or Tableau
Bonus: experience with semantic layers, data contracts, ML/LLM applications in production, or cloud infrastructure (AWS, GCP, or Azure)
Self-directed and comfortable managing own priorities in a fast-moving environment
Care about data quality, reproducibility, and documentation
Love animals

Benefits

Comp & perks

Hybrid working location
Health, Dental and Vision Insurance
Life and Disability Insurance
Flexible PTO
Mental Wellbeing Options
Robust Holiday Schedule
Registered Retirement Savings Plan
Growth Opportunities!