Data Engineer

2 days ago

Toronto, Ontario, Canada Galent Full time

Key Responsibilities

Data Engineering – 75%

  • Design, develop, and maintain scalable data pipelines using Databricks (PySpark, Delta Lake, SQL).
  • Build and optimize ETL/ELT workflows for structured and unstructured data.
  • Develop and manage Delta Lake architectures, ensuring ACID compliance and high data quality.
  • Integrate data from various sources (APIs, databases, streaming platforms, cloud storage).
  • Implement data quality frameworks, monitoring, logging, and alerting.
  • Optimize Databricks clusters, workflows, and job performance.
  • Collaborate with cloud platform teams on AWS architecture, security, and cost optimization.
  • Ensure proper data governance, documentation, and metadata standards.

Data Science – 25%

  • Develop, train, and evaluate ML models in Python using libraries like scikit-learn, pandas, and PyTorch/TensorFlow (optional).
  • Operationalize and deploy machine learning models using SageMaker (training jobs, endpoints, pipelines).
  • Perform exploratory data analysis (EDA), feature engineering, and statistical analysis.
  • Collaborate with business stakeholders to understand analytical requirements and communicate insights.
  • Partner with Data Engineering teams to productionize ML features and pipelines.

Required Skills & Experience

  • 7+ years of experience as a Data Engineer, Data Scientist.
  • Strong proficiency in Python for data processing and ML.
  • Hands-on experience with Databricks (PySpark, SQL, Delta Lake, MLflow).
  • Experience building and deploying ML models using AWS SageMaker.
  • Solid understanding of data modeling, warehouse/lakehouse architectures, and cloud-native patterns.
  • Proficiency with ETL/ELT development, CI/CD, and version control (Git).
  • Strong problem-solving mindset and ability to work cross-functionally.

Preferred Qualifications

  • Experience with AWS services such as S3, Lambda, Glue, Step Functions, IAM.
  • Familiarity with ML lifecycle management (MLflow, SageMaker Pipelines).
  • Knowledge of streaming technologies (Kafka, Kinesis) is a plus.
  • Exposure to MLOps best practices.
  • Strong communication skills and ability to translate technical concepts into business language.

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