Ravl Io2mo ago

Data Engineer

Toronto · TorontoFull Time Permanentmid

Data EngineeringData EngineerData & AI

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Quick Summary

Requirements Summary

mentor peers, contribute to data architecture decisions,

Technical Tools

Data EngineeringData EngineerData & AI

RAVL helps technologists accelerate their careers.

At RAVL, we connect strategy with execution, care deeply about the people we work with, and measure success by the lasting impact we leave behind. Our purpose is to build a team that puts real, sustainable business outcomes at the core of everything we do.

We’re here to leave our clients better than we found them, and to create a place where our people are proud to Build. Better.

As a Data Engineer at RAVL, you’ll design, build, and operate the data pipelines and platforms that power decision-making for modern organizations. You’ll bring curiosity, technical depth, and a delivery-first mindset — transforming raw data into reliable, high-quality assets that enable insight, analytics, and machine learning. You’ll work across cloud environments, designing data systems that are resilient, secure, and built for scale.

Design and build robust, scalable ETL/ELT data pipelines using modern frameworks and cloud-native tooling

Develop data models and architectures that enable efficient analytics and reporting

Implement data ingestion, transformation, and quality checks across multiple sources and domains

Leverage cloud services (AWS, Azure, or GCP) to design secure, cost-efficient, and maintainable data systems

Work closely with platform and software engineers to integrate data flows into broader application ecosystems

Ensure observability, reliability, and governance across pipelines and data products

Collaborate with analysts, architects, and stakeholders to translate business requirements into technical data solutions

At higher levels: mentor peers, contribute to data architecture decisions, and influence standards across teams

You’ll thrive in this role if you bring:

Strong experience designing and maintaining data pipelines using tools like Spark, Airflow, dbt, or similar

roficiency in SQL and one or more programming languages (Python, Java, or Scala)

Hands-on experience with cloud data ecosystems — AWS (Glue, Redshift), Azure (Data Factory, Synapse), or GCP (BigQuery, Dataflow).Understanding of modern data warehousing, streaming, and lakehouse architectures

Experience with CI/CD for data workflows and version-controlled transformations

Familiarity with data governance, cataloging, and security best practices

Consulting excellence: communicates clearly, delivers visibly, and adapts to evolving business contexts

Delivery-first – focuses on reliable, incremental progress toward business outcomes.

Structured and curious – thrives on understanding how data flows and enables value.

Collaborative – partners with cross-functional teams to align data and engineering efforts.

Influential – able to drive data quality and design conversations with technical and business peers.

Languages: Python, SQL, and optionally Java or Scala

Data Processing & Orchestration: Spark, dbt, Airflow, Kafka, or similar frameworks

Cloud Platforms: AWS, Azure, or GCP (at least two preferred)

Data Storage & Warehousing: Snowflake, Redshift, BigQuery, Databricks, Delta Lake

CI/CD & Automation: GitHub Actions, Jenkins, or Azure DevOps for data workflows