Senior Quality Engineer - Data

The Role 

As a Senior Quality Engineer - Data, you will play a pivotal role in elevating our quality practices across data products, analytics workflows, and complex distributed systems. You will be an expert within our engineering organization, deeply understanding product behaviour, source-to-consumption data flows, analytics use cases, and the systems that transform operational data into trusted customer and business insight. This role will balance hands-on individual contributor responsibilities with leading, influencing, and coaching your peers.

This senior individual contributor role is for someone who is passionate about seeing systems holistically, has a strong vision for data quality, and is eager to drive the highest standards of accuracy, completeness, reliability, performance, and analytical trust. You will leverage your deep understanding of data platforms, data pipelines, databases, reporting layers, and complex system integrations to guide and influence our development process.

Your role is not to manage a team, but to be a quality champion who partners directly with developers, data engineers, analytics engineers, product managers, and analysts. You will help teams anticipate risk, validate business logic, protect data integrity, and build more robust, scalable, and reliable data-enabled products. At the core of this role is a strong focus on the customer experience; success means our platform produces accurate, explainable, timely, and useful data that helps customers and internal teams make confident decisions.

What You Will Own 

• Own and drive end-to-end testing strategy for complex distributed systems, data pipelines, analytics workflows, and reporting experiences, proactively identifying risks, integration challenges, data quality gaps, and performance bottlenecks while establishing a clear quality vision across the product.
• Define and champion data quality standards for accuracy, completeness, consistency, freshness, uniqueness, validity, lineage, reconciliation, and analytical correctness across operational systems, data stores, transformations, APIs, and dashboards.
• Design and execute validation strategies for source-to-target data flows, including ingestion, transformation, business rules, aggregations, joins, schema changes, data contracts, exports, and downstream analytics consumption.
• Partner with Product, Design, Engineering, Data, and Analytics teams from ideation through delivery to ensure metrics definitions, acceptance criteria, test data, edge cases, and quality gates reflect the real business meaning of the data.
• Provide quality-focused input on testability, scalability, observability, and reliability during ticket grooming and feature discussions, offering insights on product resilience, data integrity, usability, and analytics trust.
• Build pragmatic automated checks and test frameworks for database validation, API validation, data reconciliation, pipeline regression testing, and analytics verification using common data and quality tools.
• Design and execute integration testing and performance testing for distributed systems and data-heavy workflows, including validation of batch, streaming, event-driven, and near-real-time data processing where applicable.
• Champion shift-left quality culture by mentoring developers, data engineers, and analytics partners on testing principles, data contracts, clear acceptance criteria, early validation, fast feedback loops, and production-quality observability.
• Maintain data-driven quality management by implementing meaningful metrics and KPIs to assess data health, pipeline reliability, test coverage, defect trends, system maturity, and measurable impact of quality initiatives.
• Partner with teams to identify systemic quality challenges such as inconsistent metric definitions, missing lineage, brittle transformations, weak test data, flaky pipeline checks, data drift, or gaps between product behaviour and analytics reporting.
• Drive innovative quality engineering approaches for data and analytics products, taking accountability for successful execution and adoption across product development teams.

Required Qualifications 

• 5+ years of progressive software engineering, quality engineering, data engineering, or analytics engineering experience, with deep focus on quality in complex, distributed system environments.
• Proven track record as a senior individual contributor with strong quality mindset and ability to influence technical direction through innovative, pragmatic, and evidence-based approaches.
• Deep hands-on experience working with data, including database validation, data profiling, data reconciliation, test data management, source-to-target testing, and validation of complex data flows.
• Strong SQL skills and practical understanding of relational and non-relational data stores, data modelling concepts, schema design, indexing, query behaviour, and common data failure modes.
• Experience validating analytics outputs such as dashboards, reports, operational metrics, customer-facing insights, semantic layers, or business KPIs, with ability to challenge definitions and trace results back to source data.
• Familiarity with modern data and analytics tooling such as Snowflake, BigQuery, Redshift, Databricks, Spark, Airflow, Kafka, Tableau, Power BI, Great Expectations, Soda, Monte Carlo, or similar tools.
• Strong automation or scripting skills, such as Python, JavaScript/TypeScript, Java, SQL, Bash, or PowerShell, with hands-on experience building testing infrastructure, automation frameworks, data checks, test harnesses, and quality utilities from the ground up.
• Practical experience integrating automated tests and quality checks into CI/CD pipelines using tools such as GitHub Actions, GitLab CI, Azure DevOps, Jenkins, CircleCI, Buildkite, or similar platforms.
• Experience defining deployment and release quality gates, including test suite selection, environment readiness checks, data migration validation, pipeline health signals, quality thresholds, reporting artifacts, and approaches for managing flaky or unreliable tests.
• Exceptional systems-thinking mindset toward quality challenges, with natural ability to see integration complexities, performance implications, reliability concerns, data dependencies, and downstream analytics impact across the product.
• Outstanding problem-solving skills with talent for root cause analysis of complex system and data behaviours, including issues caused by transformation logic, timing, orchestration, schema drift, stale data, duplication, or inconsistent business rules.
• Deep experience designing performance, scalability, and reliability testing strategies for data-heavy products, including large data volumes, long-running pipelines, concurrent usage, API throughput, and reporting latency.
• Excellent technical communication and collaboration skills to effectively guide and influence engineering peers, data partners, product managers, analysts, and business stakeholders through deep technical expertise and strategic quality insights.
• Genuine curiosity and passion for understanding product architecture, user experience flows, customer workflows, analytics use cases, and the business impact of engineering and data decisions

Reasons to join our Team

• Opportunity to be a part of a well-established, high-performance company that has been in business for over 30+ years
• Full benefits including health care, paid time off, and others
• A supportive team environment with emphasis on learning and development opportunities
• Our Promise: You will learn, grow, and be appreciated for your impact and contributions.
• Z-Factor: Our most celebrated value, you will work with a team of caring, high-performing, and passionate people who have fun supporting our vision, innovation, and continuous improvement.

Even if you don't have all of the Qualifications listed above, but feel you have what it takes to succeed in the role, we would love to hear from you!