Big Data Engineer

Job Title: Big Data Engineer

Location: Toronto, ON (Hybrid)

Estimated Duration: Fulltime

Our client is a leader global exchanges such as the Toronto Stock Exchange, Montreal Exchange, and numerous innovative organizations enhancing capital markets.  United as a global team, they connect cross-functionally, traversing industries and geographies, moving opportunity into action, advancing global economic growth, and propelling progress.  They are looking to hire a full time Big Data Engineer to join their dynamic team and environment.  The role is based out of their Toronto based HQ.  This is considered a hybrid role with three days per week expected in the office. 

We are seeking a seasoned and motivated Big Data Engineer to join our growing Enterprise Data and Advanced Analytics Platform team. The ideal candidate is an expert data pipeline builder and data wrangler who thrives on optimizing data systems and architecting them from the ground up. You are a self-directed professional, comfortable supporting the diverse data needs of multiple teams, systems, and products across a full spectrum of cutting-edge technologies.

This role reports to: Senior Manager, Data & Analytics & Enterprise Data Architect  Job Location: Hybrid (2-3 days in office) – based in Toronto, ON.

Key Accountabilities
Architect & Maintain Pipelines: Design, construct, and manage robust, large-scale, and efficient data pipeline architectures to meet complex functional and non-functional business requirements.

Process Optimization: Drive continuous internal process improvements by automating manual tasks, optimizing data delivery, and re-designing infrastructure for enhanced scalability and performance.

• Cloud Infrastructure Management: Administer and manage a comprehensive suite of AWS services, including S3, RDS, EC2, EMR, VPC, Security Groups, CloudFormation, CloudWatch, Managed Airflow, and SNS.
• Data Ingestion & ETL: Develop optimal infrastructure for Extract, Transform, and Load (ETL) operations from disparate data sources using SQL and AWS ‘big data’ technologies, with a focus on both bulk and streaming data.
• Engineering Excellence: Adhere to development best practices, ensuring meticulous code management, branching, and merging within GIT repositories.
• Operational Support: Provide on-call and off-business hours support on a rotational basis to ensure system reliability.
• Collaboration & Mentorship: Act as an effective team player and mentor to junior staff through technical reviews and brainstorming sessions, while expertly managing stakeholder expectations and inquiries.

Must Have(s)

Education: University degree, College diploma, or equivalent experience in Computer Science, Statistics, or Mathematics.

• Experience: 5-8 years of overall IT experience, with 4+ years specifically focused on Big Data technologies (Spark, Hive, Presto, etc.).
• AWS Expertise: Strong technical proficiency in AWS cloud technologies, specifically S3, EC2, and EMR.
• Data Platforms: Proven experience working with Snowflake.
• Programming: Proficiency in Python, Scala, Shell Scripting, or Java.
• Data Modeling: Solid understanding of data warehousing and data modeling techniques.
• Performance Tuning: Hands-on experience in performance tuning for Spark jobs and Hive tables.
• Agile Delivery: Experience working within Agile development methodologies.
• Soft Skills: Meticulous attention to detail, excellent analytical problem-solving skills, and superior interpersonal communication abilities.

Nice to Have(s)

Domain Knowledge: Good understanding of the Equity Markets business.

• DevOps Tools: Experience with Terraform or Ansible.
• Streaming: Experience with Kafka.
• Machine Learning: Experience designing technology stacks tailored for machine learning.
• Strategy: A strategic mindset with the ability to multitask in a fast-paced environment.