Senior Analytics Engineer
New
J
JobgetherSaaS
CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLGitSalesforceSnowflakeAirflowCI/CDData modelingdbt
Requirements
- 5+ years of professional experience as an Analytics Engineer, Data Engineer, or in a similar role, preferably within a SaaS environment.
- Deep expertise in SQL, dbt, and modern data modeling principles.
- Strong Python skills for pipeline development, API integrations, automation, and data processing.
- Experience modeling Salesforce data, including opportunities, contracts, subscriptions, cases, and field history.
- Proven experience developing custom ELT pipelines that ingest third-party API data into cloud data warehouses.
- Experience designing reconciliation models that join, deduplicate, compare, and validate data across multiple source systems.
- Hands-on experience with event-based and product usage data, using tools such as PostHog or Mixpanel.
- Experience connecting marketing data—including paid advertising, campaigns, and attribution—to product analytics.
- Experience designing and maintaining governed semantic layers, such as dbt Semantic Layer, Snowflake Cortex, or comparable technologies.
- Strong familiarity with large-scale cloud data platforms such as Snowflake, BigQuery, or Redshift.
- Experience with Git-based development workflows, CI/CD, automated testing, and data quality practices.
Responsibilities
- Own and continuously evolve the dbt analytics environment, ensuring models are performant, tested, documented, and aligned with modern data modeling practices.
- Design, maintain, and optimize Snowflake data warehouse structures and data ingestion processes.
- Develop core entities and datasets that accurately represent complex business processes, metrics, and operational logic.
- Build and maintain Python and Airflow pipelines for ingesting data from third-party APIs into the cloud data warehouse.
- Design cross-system reconciliation models to identify discrepancies, protect revenue, and improve data consistency across multiple source systems.
- Establish robust testing, observability, CI/CD, linting, code review, and approval practices for analytics pipelines.
- Standardize metric definitions and ensure consistent calculations across dashboards, analytics tools, and business functions.
- Design and maintain governed semantic views that provide reliable interfaces between business data and AI agents or LLM-powered applications.
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