Data Quality Engineer
WFA Digital Insight
As demand for high-quality data grows, driven by the increasing reliance on AI and digital tools, the role of Data Quality Engineer has become crucial. With a 27% increase in data quality specialist roles in the past year, this is a field with significant opportunities. Trilon Group, a leader in technology-enabled solutions, is seeking a skilled Data Quality Engineer to join their team. This professional will play a pivotal role in defining and enforcing data quality standards across the organization. Candidates should be aware that a strong background in data engineering and experience with data quality frameworks are highly valued in this position. Moreover, the ability to work collaboratively in a remote setting is essential.
Job Description
About the Role
The Data Quality Engineer role at Trilon Group is pivotal in ensuring the accuracy, consistency, and reliability of data used across the organization's AI and digital tools. This position is key to maintaining the trust and usability of these tools, making it a critical component of Trilon's technology-enabled future. The successful candidate will be responsible for defining, maintaining, and evolving the enterprise data quality rubric, which will involve establishing standards for data accuracy, completeness, consistency, timeliness, and reliability.The role requires close collaboration with various teams, including Data Engineers, product teams, and AI teams, to understand the impact of data quality on tool behavior and to resolve issues at their source. This is a remote position, offering the opportunity to work with a dynamic team spread across the United States. Trilon Group is committed to building a supercharged, technology-enabled future, and this role is central to that mission.
What You Will Do
- Define, maintain, and evolve the enterprise data quality rubric across all data domains.
- Establish standards for data accuracy, completeness, consistency, timeliness, and reliability.
- Ensure data quality expectations are clearly defined and consistently applied across the platform.
- Design and implement automated data quality checks within data pipelines.
- Build validation rules that detect anomalies, schema drift, missing data, and inconsistencies.
- Continuously improve validation coverage and effectiveness.
- Build and maintain observability systems for pipeline health, data freshness, and performance.
- Monitor data flows for failures, delays, and unexpected changes.
- Provide visibility into pipeline status and data quality metrics across the platform.
- Implement alerting and reporting mechanisms for critical issues.
- Diagnose data quality issues and trace them back to source systems or pipeline logic.
What We Are Looking For
- Strong data engineering fundamentals.
- Experience building data quality frameworks.
- A systematic approach to defining and enforcing standards.
- Detail-oriented with structured thinking.
- Motivation to build a data foundation that engineers trust.
- Experience with Azure services, including Azure OpenAI, Azure Machine Learning, and Azure Functions.
- Understanding of large language models, vector databases, embeddings, prompt orchestration, and model serving.
- Ability to work in a remote setting with excellent communication skills.
Nice to Have
- Experience with data observability tools.
- Knowledge of data governance best practices.
- Certification in data engineering or a related field.
Benefits and Perks
- Competitive compensation package.
- Opportunities for professional growth and development in a dynamic, technology-driven environment.
- Flexible, remote work arrangement.
- Access to cutting-edge technologies and tools.
- Collaborative, innovative team culture.
- Comprehensive health benefits.
- Generous PTO policy.
How to Stand Out
- To stand out, highlight your experience in designing scalable technical architectures for AI or machine learning solutions.
- Showcase your understanding of data quality frameworks and your ability to implement them effectively.
- Prepare examples of how you've identified and resolved data quality issues in previous roles.
- Familiarize yourself with Trilon Group's technology stack, especially Azure services, to demonstrate your ability to adapt.
- Be ready to discuss your approach to collaborating with cross-functional teams in a remote setting.
- Consider creating a portfolio that includes examples of your work in data quality engineering, such as scripts or models you've developed.
- When negotiating salary, emphasize your unique skills and experience, and research the market value of your role to make a strong case.
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