Data Engineer: Cloud Pipelines & GenAI (Hybrid)

Socket.dev; Toronto; Hybrid; CAD 70,000 - 120,000; Full time

Benefits offered by this job

Health and Life insurance coverage

Hybrid working environment

Employee Assistance Program

Extensive Perks Program

Job summary

Synpulse is seeking a Data Engineer to design, build, and optimize data pipelines and infrastructure for financial services clients. You will work closely with consultants and stakeholders to enable scalable, secure, and high-performance data solutions across multiple projects.

The role emphasizes data mesh architectures, CI/CD for data apps, and collaboration with QA to ensure data quality and regulatory alignment. Hybrid work in Toronto is offered.

Qualifications

• 5+ years of professional data engineering experience designing, building, and optimizing scalable pipelines.
• Strong SQL and Python skills with exposure to Spark, PySpark, Scala, or Java preferred.
• Hands-on with cloud platforms (AWS/Azure/GCP) and cloud-native tools (Snowflake, Databricks).
• Experience with ML models, GenAI pipelines, containerization (Docker) and orchestration (Kubernetes).
• Familiarity with data mesh principles and data governance practices.

Responsibilities

• Develop ETL/ELT workflows for diverse sources into warehouses and data lakes.
• Contribute to full‑stack platform projects and infrastructure initiatives.
• Build and maintain data assets and models for analytics and reporting.
• Architect data mesh patterns for domain-oriented data ownership.
• Collaborate with business teams and analysts to gather requirements and ensure data quality.

Skills

SQL

Python

Spark

Cloud

Docker

Kubernetes

Airflow

Data Mesh

PostgreSQL

NoSQL

Tools

Snowflake

Databricks

Airflow

Terraform

Git

Jenkins

Job description

Synpulse is seeking a Data Engineer to design, build, and optimize data pipelines and infrastructure for financial services clients. You will work closely with consultants and stakeholders to enable scalable, secure, and high-performance data solutions across multiple projects.

The role emphasizes data mesh architectures, CI/CD for data apps, and collaboration with QA to ensure data quality and regulatory alignment. Hybrid work in Toronto is offered.