About the Business: LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertically, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below, https://risk.lexisnexis.com About our Team: This group is collaborative and supportive, with members committed to each other's growth. Successful teams here are quick learners who adapt, deliver value, and work well together. You will design automation strategies for critical applications and data infrastructure, support adoption of AI tools for engineering productivity, and help engineers build, monitor, and maintain high-quality products for clinical data use cases. About the Role: A senior Data QA & automation engineer is needed to direct quality assurance activities for the enterprise data lake platform. This position involves developing thorough testing protocols, possessing substantial knowledge in both general and data quality validation, and utilizing AI tools to enhance engineering effectiveness for the team. Responsibilities: - Test Automation & Integration: architect and maintain end-to-end testing frameworks to support a variety of engineers working across various services (APIs, UI/UX, data pipelines, serverless applications). - Testing Protocol Establishment: define and inform best practices on maintaining quality metrics across our data and application platforms. - Data Quality and Validation: design and implement frameworks to help profile, monitor, proactively detect issues with critical data products. - AI-leveraged Engineering: leverage LLMs to accelerate tasks like test case generation, document creation, code review, synthetic/test data generation. Requirements: - 5+ years in quality focused automation engineering, with a strong understanding of how to set a high bar for quality for complex applications and data systems - Self-starter and ability to drive impact with minimal high-level direction - Strong programming skills in Python or node.js, and CI/CD tools - Experience building internal tools, automation scripts, and operational dashboards. - Solid understanding of microservices, APIs, and backend architecture principles to design and develop effective internal tools and operational workflows. - Experience with distributed systems and cloud computing (AWS, Azure, GCP) - Has worked with data-intensive systems (distributed datastores, data warehouses/lakes, big data processing) Preferred Qualifications - Experience with HIPAA or other compliance-heavy industries - Experience with infrastructure as code (Terraform, Cloudformation) - Background in data and analytics Technical Skills: - Programming: Python, node.js, SQL - Data Technologies: Apache Spark, Kafka, Delta Lake, Databricks, Looker - Cloud Platforms: AWS, Azure, GCP - Testing Tools: Playwright, pytest, Jest, great expectations or similar tools. - Automation: Jenkins, Gitlab CI, Github Actions, Apache Airflow - Monitoring: Datadog, Prometheus, Cloudwatch Working for you: We know that your well-being and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer: ● Private Medical/Dental Plan ● Savings Fund ● Life Insurance ● Meal/Grocery Voucher We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120. Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from job applicants. Learn more about spotting and avoiding scams here. Please read our Candidate Privacy Policy. We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. USA Job Seekers: EEO Know Your Rights.

• Develop and maintain Looker data models, including LookML, models, and explores • Build and optimize scalable, consumable data models to support business intelligence and strategic decision-making • Utilize Looker API 4.0 to enable programmatic access to dashboards, looks, and explores • Automate report generation and delivery using Looker API endpoints • Write and optimize complex SQL queries to improve performance and efficiency • Work with Snowflake, dbt, and Python to support data transformation and pipeline development • Collaborate with stakeholders to translate data requirements into scalable BI solutions • Ensure data accuracy, consistency, and performance across reporting layers

Role Description Why you will enjoy Mondays again: - Opportunity to collaborate with a diverse group of colleagues in a fun, creative environment - Progressive career journey and opportunity for advancement - Continuous development through training, mentorship and certification programs - Exposure to modern technologies across various industries in an agile environment - Remote work A Day in the Life: - Design and evolve enterprise data architecture across lakehouse, data warehouse, pipeline, and semantic layers, ensuring scalability, performance, and alignment with enterprise standards - Translate high-level architectural strategy into actionable technical plans, breaking work into epics, features, and sprint-ready stories for engineering teams - Lead technical design discussions, proactively identifying dependencies, risks, and tradeoffs to drive efficient and predictable delivery - Establish and enforce best practices for data modeling, pipeline design, data quality, and taxonomy across the platform - Review architecture and code for complex data solutions, ensuring consistency, maintainability, and optimal performance - Serve as a technical bridge across Data Engineering, Data Science, and Enterprise Architecture to ensure cohesive, end-to-end solutions - Mentor engineers by providing architectural guidance and elevating system-level thinking across the team - Design and build scalable data pipelines, dataflows, and semantic models using modern cloud-based data platforms (Microsoft Fabric / Azure) - Contribute hands-on to high-impact initiatives, particularly where architecture and implementation are closely intertwined - Support modernization efforts, including migration from on-prem SQL Server environments to cloud-based data platforms - Optimize data processing workflows for performance, reliability, and cost efficiency using modern engineering patterns - Partner with Data Scientists to productionize and operationalize machine learning models within enterprise data pipelines - Participate in release planning and cross-team coordination to ensure aligned delivery across multiple initiatives - Create and maintain clear, concise technical documentation covering architecture, standards, and key systems Qualifications - Deep expertise in modern data architectures, including lakehouse, data warehousing, data pipelines, and semantic modeling - Strong experience with Microsoft Fabric and Azure data services (e.g., Synapse, Data Factory, Azure SQL, OneLake, etc.) - Advanced proficiency in data engineering tools and languages (e.g., Python, SQL, Spark, notebooks, ETL/ELT frameworks) - Demonstrated ability to define and implement architecture standards, design patterns, and best practices across data platforms - Experience translating high-level architecture into deliverable work (epics, features, user stories) within Agile environments - Strong understanding of data modeling, data governance, and data quality frameworks - Experience designing and optimizing high-performance, scalable, and cost-efficient data pipelines - Hands-on experience with cloud migration efforts, particularly transitioning from on-prem SQL Server environments to modern cloud platforms - Familiarity with ML/AI integration into data platforms, including supporting production-ready model deployment - Ability to operate as a technical leader across multiple teams, influencing without direct authority - Strong communication skills, with the ability to clearly articulate complex technical concepts to both technical and non-technical stakeholders - Experience mentoring engineers and elevating overall engineering quality and consistency - Ability to thrive in a hybrid role (architecture + hands-on delivery) within a fast-paced, evolving environment Benefits - Regardless of your specific role, we seek individuals who are excited to explore, adopt, and evangelize AI tools and methodologies. - If you have experience in AI or a proven track record of rapidly learning and mentoring others on emerging tech, you’ll fit right in. Equal Employment Opportunity Policy Sparq is proud to offer equal employment opportunity without regard to age, color, disability, gender, gender identity, genetic information, marital status, military status, national origin, race, religion, sexual orientation, veteran status, or any other legally protected characteristic. We are committed to providing equal employment opportunities and believe in an inclusive workplace. If you require reasonable accommodations to participate in the job application or interview process, please let us know by contacting recruiting@teamsparq.com . #LI-REMOTE

• Manage and mentor a team of data developers • Define and enforce data architecture standards and governance frameworks • Partner with business stakeholders to translate business requirements into scalable data solutions • Oversee data integration strategies across internal and external systems • Ensure data quality, security, privacy, and compliance with regulatory standards • Support analytics, AI/ML, and reporting initiatives • Develop and maintain data engineering architecture documentation • Monitor and troubleshoot data engineering processes • Drive continuous improvement in data engineering practices • Provide regular updates and reports to senior management