Senior Data Engineer
About The Position
Flinks is seeking a Senior Data Engineer (Data / ML Platform) to establish data engineering as a discipline within the company. This role involves owning and evolving the data and ML platform, transforming models into reliable production services, hardening data models, and bridging the gap between data scientists and product teams. It's a high-ownership, greenfield-leaning position where the successful candidate will build and own much of the foundational infrastructure. The role is ideal for someone who enjoys making data and ML production-grade, focusing on pipelines, serving, governance, and reliability, and desires broad impact across the company's data.
Requirements
- 5+ years of hands-on Data Engineering experience designing, building, and operating production data platforms, pipelines, and warehouse solutions in a cloud environment.
- Strong experience with ETL/ELT development, data modeling, schema design, orchestration, data quality, lineage, and warehouse optimization.
- Expert SQL and strong Python skills, with the ability to build scalable, maintainable, and well-tested data solutions.
- Experience working with modern cloud-native data ecosystems, including data warehouses, event-driven architectures, distributed processing, and platform observability.
- Demonstrated ownership of production systems, including monitoring, reliability, performance tuning, cost optimization, incident response, and ongoing platform improvements.
- Ability to partner effectively with Data Science, Product, Engineering, and QA teams.
- Bachelor's degree in Computer Science, Data Engineering, Software Engineering, or a related technical field, or equivalent practical experience.
- Must be legally authorized to work in Canada.
Nice To Haves
- Experience with BigQuery, dbt, Airflow, or equivalent modern data tooling is highly desirable.
- Experience supporting machine learning workflows, feature pipelines, model-serving infrastructure, or MLOps environments is an asset.
Responsibilities
- Own and evolve the data platform, including the BigQuery warehouse, dbt transformation layers, Airflow/Cloud Composer orchestration, and Pub/Sub ingestion.
- Build and operate the ML platform, covering training pipelines (Kubeflow on Vertex AI), model serving (FastAPI behind Vertex endpoints), CI/CD, containerization, and typed contracts.
- Take operational ownership of model-serving infrastructure to ensure reliability is not solely on data scientists.
- Harden and standardize the data models the business depends on, improving schemas, fixing data quality issues, and establishing trustworthy source-of-truth feeds.
- Establish data governance and observability, bringing data outside the warehouse under proper governance and building operational metrics for products lacking them.
- Standardize data engineering practices across product lines with patterns, tooling, and pipelines for other teams to adopt.
- Partner with data science, backend, and product teams on producer-to-consumer contracts.
Benefits
- Health & Dental coverage as of Day 1
- Flexible Paid Time Off (FTO)
- Remote work environment with frequent in-person gatherings and activities.
- Career development, learning opportunities and growth
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What This Job Offers
Job Type
Full-time
Career Level
Senior
