Summary

Responsibilities

- Design, build, and maintain scalable data pipelines to support analytics, ML, and operational reporting.
- Develop robust data ingestion, transformation, and integration workflows using Python, SQL, and contemporary data engineering frameworks.
- Build and maintain batch and streaming data pipelines leveraging technologies such as Kafka (or similar pub/sub tools).
- Work with Google Cloud Platform (GCP) services, including Cloud Storage, Dataflow, Pub/Sub, BigQuery, Cloud Spanner and Cloud Functions
- Develop and manage data APIs and interfaces (REST and GraphQL) to enable high-performance data access across microservices.
- Implement CI/CD automation for data pipelines using GitHub Actions, Argo CD, or equivalent tools.
- Collaborate with Data Scientists and MLOps teams to integrate ML/NLP models into data pipelines and production workflows.
- Build and operationalize NLP data pipelines for structured and unstructured data sources (e.g., Rx claims, clinical documents).
- Enable continuous learning and model-retraining workflows using Vertex AI, Kubeflow, or similar GCP-native tooling.
- Implement frameworks for observability and data quality, ensuring ML predictions, confidence scores, and fallback events are logged into data lakes or monitoring systems.
- Support distributed data systems and ensure reliability, performance, and scalability of data infrastructure.

Required Qualifications

- 5+ years of experience building data pipelines or backend data workflows using Python, Java, or similar languages.
- 2+ years of experience designing REST/GraphQL data services or integrating data APIs.
- Hands-on experience working with ML/AI model integration in production (e.g., Vertex AI Endpoints, TensorFlow Serving, ML REST APIs).
- Experience handling structured and unstructured datasets,



including healthcare data (Rx claims, clinical documents, NLP text).
- Familiarity with the end-to-end ML lifecycle: data ingestion, feature engineering, training, deployment, and real-time inference.
- 2+ years of experience with cloud platforms (GCP preferred; AWS or Azure acceptable).
- 2+ years working with streaming platforms like Kafka or equivalent.
- 2+ years of experience with databases (Postgres or similar relational systems).
- 2+ years of experience with CI/CD tools (GitHub Actions, Jenkins, Argo CD, etc.).

Preferred Qualifications

- Direct, hands-on experience with Google Cloud Platform, especially BigQuery, Dataflow, GKE, Composer and Vertex AI.
- Knowledge of Kubernetes concepts and experience running data services or pipelines on GKE.
- Strong understanding of distributed systems, microservice patterns, and data-centric system design.
- Experience using Vertex AI, Kubeflow, or other ML orchestration platforms for model training and serving.
- Knowledge of GenAI pipelines, LLM prompt workflows, and agent orchestration frameworks (e.g., LangChain, transformers).
- Experience deploying Python-based ML/NLP services into microservice ecosystems using REST, gRPC, or sidecar architectures.
- Domain experience in healthcare, claim adjudication, or Rx data processing.

Education

- Bachelor’s degree in Computer Science, Data Engineering, Information Systems,



or equivalent experience (High School Diploma + 4 years of relevant experience acceptable).

Requirements

- 5+ years of experience building data pipelines or backend data workflows using Python, Java, or similar languages.
- 2+ years of experience designing REST/GraphQL data services or integrating data APIs.
- Hands-on experience working with ML/AI model integration in production (e.g., Vertex AI Endpoints, TensorFlow Serving, ML REST APIs).
- Experience handling structured and unstructured datasets, including healthcare data (Rx claims, clinical documents, NLP text).
- Familiarity with the end-to-end ML lifecycle: data ingestion, feature engineering, training, deployment, and real-time inference.
- 2+ years of experience with cloud platforms (GCP preferred; AWS or Azure acceptable).
- 2+ years working with streaming platforms like Kafka or equivalent.
- 2+ years of experience with databases (Postgres or similar relational systems).
- 2+ years of experience with CI/CD tools (GitHub Actions, Jenkins, Argo CD, etc.).

Nice to have Skills

- Direct, hands-on experience with Google Cloud Platform, especially BigQuery, Dataflow, GKE, Composer and Vertex AI.
- Knowledge of Kubernetes concepts and experience running data services or pipelines on GKE.
- Strong understanding of distributed systems, microservice patterns, and data-centric system design.
- Experience using Vertex AI, Kubeflow, or other ML orchestration platforms for model training and serving.
- Knowledge of GenAI pipelines, LLM prompt workflows, and agent orchestration frameworks (e.g., LangChain, transformers).
- Experience deploying Python-based ML/NLP services into microservice ecosystems using REST, gRPC, or sidecar architectures.
- Domain experience in healthcare, claim adjudication, or Rx data processing.

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📌 Data Engineer (Canada)
🏢 Intuitive
📍 Canada