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BigData Quality Engineer

Job in Halifax, Nova Scotia, Canada
Listing for: 0000050007 Royal Bank of Canada
Full Time position
Listed on 2026-06-04
Job specializations:
  • IT/Tech
    Data Engineer, Data Analyst
Job Description & How to Apply Below

Job Description

What's the opportunity?
As part of the Global Functions Technology (GFT) within RBC's Technology and Operations division, you'll be at the heart of a team that extends its services across the organization, offering IT solutions that drive transformation and efficiency. Our collaborative efforts span various domains including Risk, Finance, HR, CAO, Audit, Legal, Compliance, Financial Crime, Capital Markets, Personal and Commercial Banking, and Wealth Management.

Moreover, we're at the forefront of creating digital tools and platforms that foster better collaboration across the  this dynamic environment, you'll have the opportunity to work closely with leadership that values the recognition of achievements and the sharing of insights across teams to fuel continuous improvement.

Joining our Finance IT Data As a Service team, you'll play a supporting role in Test Implementation and Test Automation on our cutting-edge Big Data Platform. This role involves working with the latest technologies and programming languages such as Cloudera, Spark, Scala, Unix, SQL, Python, Databricks, and leveraging GenAI and Agentic AI.Your primary responsibility will be to support QE Automation for our diverse client groups - executing tests, building automation scripts, and validating data pipelines under the guidance of senior engineers.

This position offers a unique chance to develop your technical expertise in big data testing, finance data, and AI-driven quality engineering, all while delivering IT solutions in a fast-paced and ever-changing business landscape.
What you will do?
  • Execute test cases for data pipelines from ingestion to consumption, validating transformations, aggregations, and business rules across HDFS, Hive, Spark, and Databricks.

  • Validate ETL/ELT logic using Python (PySpark) and SQL - joins, filters, lookups, derived columns, and data mappings; perform source-to-target reconciliation.

  • Develop and maintain automated test scripts using AI, Python (Pandas, PySpark) and SQL

  • Contribute to automated test suites with CI/CD integration (Git Hub Actions, Ansible Automation Platform; automate schema validation, row count checks, and data comparisons across environments.

  • Execute data quality checks - completeness, accuracy, consistency, and timeliness - on finance datasets

  • Test Spark jobs, Hive queries, and Data Lake pipelines; validate data landing, partition management, file integrity, and data freshness.

  • Leverage Generative AI tools (Git Hub Copilot, Windsurf) to accelerate test case generation, SQL writing, and script development; support building Agentic AI workflows for autonomous test execution and intelligent triage.

  • Validate data feeding into ML models and test AI-generated outputs for correctness; support feature store and training data quality validation.

  • Understand and test finance data attributes:
    Trades, booking entities, currencies, GL accounts, product types, settlement dates; support regulatory and management reporting feed validation.

  • Partner with data engineers, SRE, and product teams to support testability and observability; log defects with clear root cause analysis in JIRA; maintain test documentation in Confluence.

  • Participate in Agile/Scrum ceremonies and contribute to shift-left testing practices and continuous testing workflows.

  • What you’ll impact
  • Reliability and accuracy of critical data processing that powers Finance, Risk, Audit, Compliance, and enterprise reporting.

  • Faster, predictable releases through standardized automation, early validation, and AI-enabled quality acceleration.

  • Reduced operational risk via proactive defect prevention, audit-ready artifacts, and governance-aligned controls.

  • What you’ll need to thrive
    Must-Haves
  • 1-2 years hands-on experience in QE, data testing, or data engineering in Big Data environments.

  • Strong foundation:
    Bachelor's degree in Computer Science, Engineering, Finance, or equivalent experience.

  • SQL proficiency:
    Spark SQL, Databricks SQL, Hive, Trino - writing and debugging queries for data validation and reconciliation.

  • Code & scripting:
    Python (PySpark, Pandas), Shell scripting, SQL; ability to build and maintain automated test scripts.

  • Big Data…

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