Data Engineer

Toronto,ON Onsite

This is 3 months contract

Overview

We are seeking a highly skilled Data Engineer with complementary Data Science expertise to design, build, and optimize data platforms that power advanced analytics and machine learning solutions. The ideal candidate has deep hands-on experience with Databricks, Python, and AWS SageMaker, and thrives in a fast-paced environment where both engineering excellence and analytical curiosity are valued.

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.

Robust communication skills and ability to translate technical concepts into business language.

📌 Data Engineer (Toronto)
🏢 Recutify
📍 Toronto