Senior Analytics Engineer
New
C
Coconut Software FinTech
Canada - RemoteContractSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- SQLArtificial IntelligenceMicrosoft Power BITableauPostgresSparkdbtRedshiftDatabricksLookerLLM
Requirements
- 5+ years of applied experience in an Analytics Engineering or Data role
- Solid track record of technical leadership in B2B/SaaS environments
- Extensive experience architecting dbt projects
- Managing complex Looker environments
- Solid experience using Looker (or similar BI tools like Tableau or Power BI) to build dashboards and solve complex data requests
- Strong architectural understanding of modern data stacks
- Proven experience in Databricks
- Expertise in writing, improving, and troubleshooting complex SQL queries
- Passion for learning and exploring how AI/LLMs (like Google Gemini) can be integrated into the data lifecycle
- Ability to successfully collaborate cross-functionally, gathering requirements and delivering results
- Excellent interpersonal and communication skills
Responsibilities
- Mentor and support data analysts, data engineers and developers, fostering a collaborative and psychologically safe environment.
- Set high standards for technical excellence in SQL, dbt modeling, and LookML architecture.
- Collaborate on the migration of legacy logic from Redshift/Postgres into performant Databricks silver and gold layers.
- Independently implement complex data models in dbt, demonstrating expertise in modularity, testing, and performance optimization on Spark.
- Own the LookML layer, ensuring explores and dashboards are performant, intuitive, and accurate.
- Act as a "Hybrid Analyst," balancing long-term engineering projects with ad-hoc requests to support the business.
- Translate stakeholder questions into Looker-based reports and visualizations.
- Lead technical data planning through discovery, design, and release, ensuring clear documentation for data catalogs and version tracking.
- Identify opportunities for "data debt" reduction and architectural improvements in the Lakehouse and semantic layers.
- Proactively address system characteristics like latency and throughput, ensuring data pipelines are optimized for cost and speed.
- Act as a subject matter expert in your data domain, contributing to business strategy.
- Provide engineering perspectives on the product roadmap, clearly communicating data feasibility and reporting limitations.
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