• Define and maintain the enterprise data platform blueprint, including ingestion, storage, processing, serving, governance, and security. • Design, model, and simulate complex business processes using BPMS tools and workflow frameworks. • Establish and govern architectural standards, including data contracts, service-level objectives, access controls, and privacy-by-design principles. • Develop enterprise and domain reference architectures to support scalability, interoperability, and reuse. • Design and modernize systems into cloud-compatible or cloud-native architectures, including migration strategies. • Lead architecture reviews for complex or high-risk initiatives, evaluate trade-offs, and mitigate technical and compliance risks. • Develop process architecture artifacts, including workflow diagrams, functional maps, and cross-functional process models. • Recommend and implement best practices for process orchestration, workflow design, and system integration. • Align data and process architecture with enterprise architecture, security, IAM, networking, and FinOps practices. • Evaluate build-versus-buy decisions, serve as a subject matter expert in BPMS tools and process design patterns, collaborate with stakeholders to test and refine workflows, mentor technical professionals, provide strategic architecture recommendations, support CI/CD, DevOps, and automation practices, ensure solutions meet evolving security requirements and industry standards, and perform other duties as assigned.

• Assists in the development of large-scale data structures and pipelines • Applies understanding of key business drivers to accomplish own work • Writes ETL processes, designs database systems • Collaborates with cross-functional teams to transform data • Uses knowledge of cloud data platforms and experience designing SQL queries

Role Description We are seeking a Data Quality Engineer to build and operate the tooling that makes data quality measurable, automated, and continuous. This role builds the instrumentation — automated checks, monitoring, and dashboards that test governed data against rules at scale and surface issues the moment they appear. This is a hands-on engineering role embedded in a lean quality function. You will translate governance rules and quality thresholds into executable tests, integrate them into data pipelines and platforms, and give the organization always-on visibility into the health of its data. You will partner with Governance, Data Operations, and platform teams to embed quality into how data moves rather than checking it after the fact. Responsibilities - Translate authored data rules and quality thresholds into automated, executable quality checks. - Build and maintain data quality monitoring, alerting, and dashboards across governed datasets. - Integrate quality checks into data pipelines, classification workflows, and governance tooling. - Automate detection of rule conflicts, duplicates, and reference data anomalies at scale. - Develop tooling that supports auditors, analysts, and stewards in their day-to-day work. - Instrument human-in-the-loop workflows to capture validation outcomes and quality signals. - Maintain reliable, performant data quality infrastructure and supporting data flows. - Partner with Governance to make rules executable and to close gaps between definition and enforcement. Success in this role will be measured by: - Automated Coverage – Growing share of rules and thresholds enforced by automated checks rather than manual review. - Timeliness – Quality issues surfaced in near-real-time through monitoring and alerting. - Reliability – Stable, performant quality infrastructure and trustworthy metrics. - Enablement – Tooling that measurably improves auditor, analyst, and steward productivity. - Embeddedness – Quality checks integrated into production pipelines and workflows, not bolted on. - Issue Reduction – Measurable decline in quality issues reaching downstream consumers. Qualifications - 4–8 years in data engineering, data quality engineering, analytics engineering, or a related field. - Strong programming skills (e.g., Python, SQL) and experience building data pipelines or automation. - Experience implementing automated data quality checks, testing, or monitoring frameworks. - Familiarity with data quality dimensions and how to operationalize them in code. - Comfort working with cloud data platforms, APIs, and governance/quality tooling. - Ability to translate written rules and standards into executable logic. - Exposure to AI/ML-assisted classification and human-in-the-loop systems a plus. - Strong communication skills and a collaborative, enablement-oriented mindset. Company Description At MediaRadar, we are committed to creating an inclusive and accessible workplace where everyone can thrive. We believe that diversity of backgrounds, perspectives, and experiences makes us stronger and more innovative. We are proud to be an Equal Opportunity Employer and make employment decisions without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, or any other legally protected status. We are also committed to ensuring our recruitment process is accessible to all applicants. If you need a reasonable accommodation during the application or interview process, please contact us at careers@mediaradar.com. We’re excited to meet people who share our values and want to build the future with us.

• Participate in technical interviews (system design + deep dive) • Ask scenario-based questions focused on real production and regulatory challenges • Evaluate candidate answers for practical experience vs. theoretical knowledge • Review take-home tasks or architectural diagrams if applicable • Provide clear, structured written feedback with a hire / no-hire recommendation