Staff Data Engineer building pipelines, models, and infrastructure for exacare ai’s post - acute care AI platform. Turning healthcare data into reliable foundations for product, analytics, and ML workflows.
About the role
- Senior Data Engineer developing and managing Flinks' data platform to power their financial intelligence products. Collaborating across teams to establish data governance, observability, and reliability.
Responsibilities
- Own and evolve the data platform - the BigQuery warehouse, dbt transformation layers, Airflow / Cloud Composer orchestration and Pub/Sub ingestion that feed every model and metric.
- Build and operate the ML platform - 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 so reliability isn't carried by the data scientists alone.
- 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 - bring data that lives outside the warehouse under proper governance and build operational metrics for products that don't yet have them.
- Standardize how data engineering is done across product lines - patterns, tooling and pipelines other teams can adopt.
- Partner across data science, backend and product on the producer to consumer contract (models produced by data science, consumed/aggregated downstream, surfaced to clients).
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 that support both operational and analytical workloads.
- 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.
- Experience supporting machine learning workflows, feature pipelines, model-serving infrastructure, or MLOps environments is an asset.
- Ability to partner effectively with Data Science, Product, Engineering, and QA teams to deliver trusted, scalable, and well-governed data solutions.
- 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.
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
- And more
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