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Surrey, British Columbia, Canada
Hybrid
Responsibilities
Provide Tier 4 production support for data platforms, pipelines, integrations across Snowflake, Databricks, Azure Data Factory, Azure Data Lake, and SQL databases.
Triage, investigate, resolve, and document incidents, service requests, defects, data quality issues, performance problems, failed jobs, and access-related requests.
Support Snowflake operations including databases, schemas, warehouses, roles, access patterns, query troubleshooting, performance tuning, cost awareness, secure data sharing, and operational governance.
Perform root cause analysis, contribute to problem management, and recommend preventive actions to reduce recurring incidents and improve service reliability.
Create and maintain support documentation, runbooks, knowledge articles, recovery procedures, escalation paths, and operational handover materials.
Use AI-assisted tools responsibly to accelerate support activities such as log analysis, query troubleshooting, documentation, ticket summarization, anomaly detection, and knowledge discovery while maintaining privacy, security, and quality standards.
Collaborate with development and engineering teams on fixes, releases, change validation, operational acceptance, deployment readiness, and post-release support.
Drive continuous improvement in monitoring, alerting, automation, support processes, incident response, access management, data quality checks, and operational stability.
We’d love to hear from you if you have
At least 4 years of practical experience development and/or supporting production data platforms, data pipelines, integrations, and operational data services using Snowflake, Databricks, Azure Data Factory, Azure Data Lake, SQL, and modern ELT/ETL patterns.
Hands-on experience troubleshooting Snowflake workloads, including SQL queries, warehouses, schemas, roles, permissions, performance, data loads, and operational failures.
Strong SQL skills with the ability to investigate data discrepancies, failed jobs, slow queries, access issues, and production incidents.
Experience working with monitoring, alerting, logging, job scheduling, data quality checks, and operational dashboards for data platforms and pipelines.
Good understanding of support practices including incident management, request fulfilment, change support, problem management, root cause analysis, escalation, and knowledge management.
Working knowledge of Python, PySpark, scripting, Git, CI/CD concepts, and automation practices sufficient to troubleshoot and support production data solutions.
Awareness of how AI can be used responsibly to improve data support activities, including ticket triage, log analysis, anomaly detection, documentation, and operational knowledge discovery.
Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or equivalent practical experience.
Excellent communication skills, strong ownership, customer focus, analytical troubleshooting ability, attention to detail, and a proactive team-oriented mindset.
At Finning, we prioritize creating a diverse and inclusive environment. We are proud to be an equal opportunity employer, and we actively encourage all individuals to express themselves and achieve their full potential. As a company, we continuously strive to enhance our outreach to individuals of all backgrounds and identities. We do not discriminate against applicants based on gender identity, race, national and ethnic origin, religion, age, sexual orientation, marital and family status, and/or mental or physical disabilities. Furthermore, Finning is committed to collaborating with and providing reasonable accommodations /adjustments to individuals with disabilities. If you require an adjustment/accommodation at any point during the recruitment process, please inform your recruiter.