Senior Data Engineer
| Company | Wave Financial |
|---|---|
| Location | Toronto, ON, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Senior |
Requirements
- 6+ years of experience in building data pipelines and managing a secure, modern data stack
- At least 3 years of experience working with AWS cloud infrastructure, including Kafka (MSK), Spark / AWS Glue, and infrastructure as code (IaC) using Terraform
- Write and review high-quality, maintainable code that enhances the reliability and scalability of our data platform, using Python, SQL, and dbt extensively
- Prior experience building data lakes on S3 using Apache Iceberg with Parquet, Avro, JSON, and CSV file formats
- Experience with Airflow or similar orchestration systems to build and manage multi-stage workflows that automate and orchestrate data processing pipelines
- Familiarity with data governance practices, including data quality, lineage, and privacy, as well as experience using cataloging tools to enhance discoverability and compliance
- Experience developing and deploying data pipeline solutions using CI/CD best practices to ensure reliability and scalability
- Working knowledge of tools such as Stitch and Segment CDP for integrating diverse data sources into a cohesive ecosystem
Responsibilities
- Designing, building, and deploying the components of a modern data stack, including CDC ingestion (using Meltano or similar tools), a centralized Iceberg data lake, and a variety of batch, incremental, and stream-based pipelines
- Helping build and manage a fault-tolerant data platform that scales economically while balancing innovation with operational stability by maintaining legacy Python ELT scripts and accelerating the transition to dbt models in Redshift, Snowflake, or DataBricks
- Collaborating within a cross-functional team in planning and rolling out data infrastructure and processing pipelines that serve workloads across analytics, machine learning, and GenAI services
- Independently identifying opportunities to optimize pipelines and improve data workflows under tight deadlines
- Responding to alerts and proactively implementing monitoring solutions to minimize future incidents, ensuring high availability and reliability of data systems
- Providing technical assistance and communicating effectively with stakeholders to address their concerns
- Assessing existing systems, optimizing data accessibility, and providing innovative solutions to help internal teams surface actionable insights that enhance external customer satisfaction
Preferred Qualifications
- Knowledge and practical experience with Athena, Redshift, or Sagemaker Feature Store to support analytical and machine learning workflows is a definite bonus!