Senior Data Engineer

Coconut SoftwareFinancial software

Location: Canada - Remote; Opportunity to work remote - anywhere in Canada!ContractSenior

Salary125,000 - 165,000 CAD per year

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Job Details

Experience: 5+ years in data engineering, including 2+ years running Databricks in production
Required Skills: AWSPythonSQLTerraformdbtDatabricks

Have 5+ years of data engineering experience.
Have 2+ years running Databricks in production, including Delta, Jobs, DLT/Lakeflow, and Unity Catalog.
Bring strong Python, SQL, and dbt skills.
Have experience replicating from operational databases using CDC or events and ingesting messy files.
Have participated in at least one warehouse or lakehouse migration.
Be comfortable with AWS data services.
Have handled PII or regulated data; financial services experience is ideal.
Have hands-on experience using AI coding agents such as Claude Code for pipelines, dbt, debugging, or troubleshooting.
Have worked with infrastructure-as-code, such as Terraform or DAB, to manage data infrastructure.
Have experience with LookML or embedded Looker.
Have managed pipelines feeding forecasting or ML models.
Be able to work autonomously, take initiative, and lead projects.

Build and run production pipelines in Databricks using Lakeflow, Jobs, and dbt, from customer-file ingestion through tables ready for models and reporting.
Lead the next phases of the Redshift-to-Databricks migration, including landing source tables, moving product analytics onto the lakehouse, and retiring old warehouses.
Own replication and ingestion from production databases across multiple environments using AWS DMS, Databricks Lakeflow Connect, and Fivetran.
Set data contracts with internal teams and external ML partners to prevent schema changes from breaking downstream systems.
Provide product teams with effort estimates and trade-offs, and push back on scope when needed.
Monitor pipeline health, data quality, and Databricks cost, and participate in the Data on-call rotation.
Document runbooks, lineage, and design decisions so the team can manage the systems.
Contribute to the team's use of AI across Engineering, learning and applying technology to solve customer and business problems.

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