Data Quality Engineer, (Consultant, Data & Analytics)

Fearless•Baltimore, MD

2d•$96,132 - $162,462•Remote

About The Position

We are seeking a Data Quality Engineer as part of our Consultant, Data & Analytics career path to support a large federal agency's enterprise data modernization initiative. This role focuses on building automated validation processes and monitoring capabilities that ensure reliability across a secure, cloud-native data platform serving thousands of users across the agency. You will combine hands-on engineering with stakeholder collaboration to translate data quality findings into actionable improvements that strengthen trust in enterprise data services. This is a fully remote role. At Fearless, the consultant is responsible for leveraging data to answer business questions, develop algorithms and models, and build scalable infrastructure for managing and processing data. This role will possess and/or develop the skills to be able to span the domains of data analysts, engineers, and scientists at a practical application level of proficiency. This Consultant role will work on projects that allow them to design solutions that support decision-making, optimize operations, and enable data-driven innovation.

Requirements

Minimum of 4 years of experience in data engineering, data quality, or related analytics roles.
Bachelor's degree in Computer Science, Information Systems, Data Science, or related field, or equivalent work experience.
Ability to obtain and maintain Public Trust clearance.
Experience developing automated data validation, testing, and monitoring solutions for enterprise data platforms.
Strong SQL and ETL/ELT experience with the ability to analyze, troubleshoot, and improve complex data pipelines.
Experience working with cloud-based data platforms and data observability or monitoring tools.
Experience defining, implementing, and maintaining data quality rules, standards, or validation frameworks.
Experience translating business requirements into technical data quality rules and validation processes.
Demonstrated ability to communicate technical concepts, data quality findings, and recommendations to business stakeholders and cross-functional teams.
Experience working in a consulting or client-facing environment, balancing hands-on engineering with stakeholder collaboration, requirements gathering, and problem solving.

Nice To Haves

Hands-on experience with Databricks, Azure Data Factory, or similar cloud data engineering platforms supporting enterprise analytics workloads.
Experience supporting federal government, healthcare, or other highly regulated environments with strong data governance and quality requirements.

Responsibilities

Design and implement automated data quality validation and monitoring processes across cloud data pipelines.
Validate, monitor, and troubleshoot enterprise data pipelines within the organization’s cloud data platform.
Investigate, troubleshoot, and resolve data quality issues while identifying opportunities to improve reliability and observability.
Collaborate with data engineering teams to embed data quality and validation into new pipelines and data products.
Partner with business stakeholders and engineering teams to translate data quality findings into actionable recommendations and technical solutions.
Define and maintain data quality rules, standards, metrics, and reporting to support trusted enterprise data products.
Communicate data quality risks, findings, and recommendations to both technical and non-technical audiences.
Support continuous improvement initiatives that strengthen data pipeline reliability and platform observability.