Preferred Travel Group

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking an experienced, analytical, detail‑oriented, and quality‑driven Data QA Engineer III. The Data QA Engineer will design and execute automated tests, implement data quality monitoring and alerting, and perform targeted manual verifications to ensure the accuracy, reliability, and trustworthiness of data engineering deliverables. This role focuses on validating data pipelines, transformations, data models, and BI/report outputs (not UI test automation) across ETL/ELT pipelines, reports, dashboards, and data management applications—so the business can confidently use data to drive decisions. Under the general supervision of the Manager of QA, the QA Data Engineer works closely with developers in the Data Engineering team. Key Responsibilities - Planning - Collaborate with the engineering team to refine work requests in an agile development system, translating requirements into testable data quality criteria. - Develop comprehensive test plans and test cases as part of project planning, including test strategy for validation, reconciliation, regression, and monitoring. - Provide accurate estimates of effort and duration of QA tasks. - Testing - Create and maintain automated tests to validate pipeline requirements, transformation logic, and downstream analytics/report outputs. - Write and optimize SQL queries for automated validations (such as row counts, uniqueness, referential integrity, reconciliation, business-rule checks, etc.). - Build regression suites for critical datasets and dashboards to ensure consistent numbers across releases and backfills. - Create and maintain deterministic test datasets (fixtures) and “golden” expected results for repeatable validation. - Assist with the verification and recreation of user-reported data issues, including data lineage/traceback from report to source. - File detailed and actionable defect reports, including reproduction steps, expected outcomes, and evidence (queries, sample records, screenshots of report values when relevant). - Work collaboratively with engineers to troubleshoot defects, validate fixes, and prevent recurrence via new tests and monitoring. - Continuously improve QA processes, frameworks, and tools for data testing and validation to align with best practices. - Integration & Monitoring - Integrate test automation with deployment automation, work tracking, and test tracking systems to enforce automated quality gates. - Schedule and manage automated test runs (PR/CI, nightly, and post-deploy), ensuring consistent and reliable execution. - Implement data observability checks and alerting for freshness, volume, distribution/anomaly detection, and schema drift; tune alert thresholds to reduce noise. - Collect, consolidate, and analyze test and monitoring results to identify trends, systemic issues, and opportunities to improve data reliability. - Define and develop key performance indicators (KPIs) for measuring test effectiveness (coverage, escaped defects, time-to-detection, time-to-resolution). - Collaboration - Manage and prioritize work using the ticketing system while maintaining regular communication in stand-ups and stakeholder meetings. - Conduct code reviews of test code, SQL validation logic, and monitoring rules to ensure adherence to best practices and high-quality deliverables. - Partner with Data Engineering and BI stakeholders to validate semantic models and report logic (e.g., dataset/model measures, transformations, refresh behavior). - Contribute to technical documentation of processes, tools, workflows, and standards. - Leadership & Mentorship - Mentor other team members by sharing knowledge, conducting training sessions, and providing guidance on best practices for data testing and quality. - Take ownership of complex or high-impact initiatives (e.g., establishing regression strategy, monitoring standards), ensuring timely delivery and alignment with business objectives. Qualifications - Bachelor’s degree in Computer Science, Information Systems /other relevant degree or equivalent professional experience. - Demonstrated experience writing automated QA tests, especially for back-end systems without UI. - Expert knowledge of relevant languages, such as SQL, Python, JavaScript, and/or C#. - Expert knowledge of test automation tools such as Cypress, Great Expectations. - Excellent analytical and problem-solving skills with a high level of attention to detail. - Strong communication and collaboration skills to work effectively with cross-functional teams. - Experience with CI/CD pipelines and version control systems (e.g., Jenkins, Git). - Knowledge of business intelligence tools such as Power BI or Tableau. - Understanding of Agile development processes. Typical Behaviors & Working Style - Versatile and adaptable, flexing to meet the needs of the situation. - Maintains people-orientation, even if reserved in nature. Must be helpful and service-oriented, with a strong focus on repeatable, high-quality results. - Decision-making is collaborative, but meticulous, requiring consideration of facts, established procedures, and proven processes. - Communicates based on the task or technical needs at hand, defining clear team roles. - Leads according to specialty or expertise. Will act with conviction to ensure quality standards, rarely delegating. Preferred Working Environment & Job Characteristics - Experienced data quality environment, contributing to the validation of pipelines, transformations, and analytics outputs with a strong sense of ownership and care. - Quality‑focused setting where accuracy, reliability, and trust in data are highly valued and directly support business decision‑making. - Dynamic and collaborative workflow, balancing planned QA initiatives, monitoring, and issue investigation alongside Data Engineering and BI partners. What success in this role looks like - Data pipelines, models, and analytics outputs are accurate, reliable, and widely trusted, enabling confident decision‑making across the business. - Data quality issues are identified and resolved proactively, through automated testing, monitoring, and efficient root‑cause analysis, with minimal disruption. - The data quality function grows stronger over time, through robust QA frameworks, clear documentation, knowledge‑sharing, and ownership of high‑impact initiatives. Working Conditions This is a work from home position. All technology required will be provided. Training - Orientation via some live remote and some pre-recorded video sessions. - IT security training. - Internal development process and procedures. - Company-approved AI technology. Salary $100,000 - $130,000 actual compensation within this range will be determined by multiple factors including candidate experience and expertise.

About Us At Preferred Travel Group, we care deeply about our people, nurture independence, and celebrate individuality. Family values inspire us, and we believe that change creates opportunity. We are committed listeners and deliberate storytellers in hospitality. We engineer potential, foster trust, and co-create brighter futures. Our culture values collaboration, adaptability, and precision—qualities essential to every role. We are forever curious, guided by the Pineapple as our global symbol of hospitality. We believe the business of hospitality is borderless, and we proudly embrace that spirit every day. We believe that every team member brings unique strengths to the table, and we’re committed to creating an environment where those strengths can thrive. ________________________________________ Position Summary We are looking for a strategic, hands‑on, and people‑focused Manager, Data Engineering to lead a cross‑functional technical team responsible for designing, building, and supporting the company’s data and analytics estate. This role balances people leadership, technical oversight, and delivery management, ensuring the team executes effectively while adhering to company standards, architectural best practices, and long‑term scalability goals. Under the general supervision of the department director, the Manager, Data Engineering leads a team of technical professionals and plays a critical role in shaping how data solutions are delivered across the organization. The manager collaborates closely with engineering and business partners, serving as the primary representative for the data engineering team and ensuring strong two‑way communication—translating business needs into clear, actionable work while ensuring the team understands organizational priorities, expectations, and outcomes. ________________________________________ Key Responsibilities People Leadership & Team Management - Lead, coach, and develop a high-performing, collaborative technical team through regular 1:1s, feedback, goal setting, and performance reviews. - Manage team capacity, schedules, time-off planning, and on-call/support rotations as applicable. - Foster a positive, inclusive, and upbeat team culture; build cohesion across varied disciplines and experience levels. - Encourage continuous learning through mentorship, training plans, knowledge sharing, and support for skill development and career growth. Technical Leadership & Architecture Oversight - Maintain awareness of the department’s overall data estate, ensuring team decisions align with long-term strategy and do not create future constraints. - Contribute to, review, and approve technical designs, ensuring security, performance, scalability, maintainability, and cost considerations are addressed. - Document and ensure adherence to internal engineering patterns and standards (naming, code structure, deployment practices, observability, documentation, security, etc.). Delivery & Agile Project Leadership - With the project manager, lead agile ceremonies and delivery practices, including stand-ups, backlog refinement, planning, retrospectives, and stakeholder check-ins. - Partner with stakeholders to translate business priorities into actionable roadmaps and well-defined tickets. - Ensure appropriate task sizing, estimation, prioritization, and clear acceptance criteria to support predictable execution. - Manage delivery risk: proactively surface blockers, dependencies, scope concerns, and trade-offs; keep work moving while protecting quality. Stakeholder Communication & Representation - Serve as the primary voice for the team outside the team and the primary voice of the department/business within the team. - Maintain clear, consistent communication with business partners and technical stakeholders regarding status, timelines, risk, and outcomes. - Partner with stakeholders to clarify requirements, align on priorities, and ensure delivered solutions provide measurable business value. Operational Support & Quality - Ensure high availability and reliability of team-owned systems through monitoring, alerting, incident response practices, and root-cause resolution. - Drive quality through code reviews, testing standards, release practices, and post-implementation validation. - Establish and maintain operational documentation and runbooks to support consistent support and knowledge transfer. Hands-on Support - As needed, and as a last resort, contribute directly to technical work (design, debugging, code reviews, small fixes) to unblock delivery, support critical incidents, or guide best-practice implementation—while keeping the primary focus on leadership and oversight ________________________________________ Required Experience/Qualifications This position is classified as a Manager role and requires significant prior experience in a data engineering–related field, including people leadership, in addition to the following: - Bachelor’s degree in Computer Science, Information Systems, or other relevant degree/related field, or equivalent professional experience - Expert knowledge of modern data and analytics platforms - Expert knowledge of relevant languages, such as SQL, dbt, Python, and/or C# - Expert knowledge of cloud data platforms and tooling (Azure/Fabric preferred) - Experience with Machine Learning, Predictive Analytics, and/or Artificial Intelligence projects - Demonstrated ability to lead predictable delivery using agile practices. - Proven ability to mentor and grow technical team members and build team culture. - Excellent verbal and written communication skills. ________________________________________ Typical Behaviors & Working Style The ideal candidate will demonstrate the following behavioral traits: - Preference for varied tasks or projects. Comfortable balancing multiple simultaneous projects in a fast-paced environment. - Focus is on technical and analytical work. Must adhere to established standards and guidelines, troubleshooting problems based on expert knowledge. - Needs to make decisive, quick decisions within a pre-defined span of control, adhering to accepted quality standards, policies, and procedures. Will also train others to make and uphold accurate decisions. - Communicates based on the task or technical needs at hand, defining clear team roles. Minimal collaboration is required, although they must prioritize specific tasks or problems. - Leads according to specialty or expertise. Will act with conviction to ensure quality standards, rarely delegating. When delegation is required, follow up will be close. ________________________________________ Preferred Working Environment & Job Characteristics This role is best suited to someone who thrives in a: - Leadership focused data engineering environment, taking ownership of team performance, technical direction, and delivery outcomes. - High standards, quality driven setting, where scalable architecture, reliability, security, and governance are non negotiable. - Fast paced, multi priority environment, balancing people leadership, delivery oversight, stakeholder needs, and operational demands. The ideal candidate will find great satisfaction in: - Leading and developing high‑performing technical teams, fostering collaboration, accountability, and continuous improvement. - Setting technical direction and engineering standards, ensuring data solutions are robust, scalable, and aligned with long‑term strategy. - Partnering with business and technical stakeholders, translating complex needs into clear priorities and measurable outcomes. - Driving consistent, high‑quality delivery, while maintaining operational excellence and supporting business‑critical systems. ________________________________________ What success in this role looks like - A high‑performing data engineering team delivering consistently, with clear direction, accountability, and strong engagement. - Reliable, scalable, and well‑governed data platforms that support critical business decisions. - Business needs translated into clear technical outcomes, delivering measurable value. - Strong stakeholder trust, built through clear communication, predictable delivery, and high standards. ________________________________________ Working Conditions This is a fully remote position. All technology required will be provided. ________________________________________ Training - Orientation via some live remote and some pre-recorded video sessions - IT security training - Internal development process and procedures - Company-approved AI technology ________________________________________ Disclaimer The above information is designed to indicate the general nature and level of work performed. It is not intended to contain or be interpreted as a comprehensive inventory of all duties, responsibilities, and qualifications required of employees assigned to this job. ________________________________________ Salary $150,000 - $180,000 actual compensation within this range will be determined by multiple factors including candidate experience and expertise.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking an analytical, experienced, and solution-oriented Data Engineer III. The Data Engineer III is responsible for developing and optimizing the company’s data pipelines, integrations, and reporting solutions to ensure efficient and reliable data operations. This role requires independent problem-solving, proactive improvement of processes, and collaboration across teams to deliver impactful data solutions. - Design, build, and maintain scalable data pipelines and ETL workflows. - Write advanced SQL queries and implement optimization techniques for performance. - Leverage Microsoft Azure & Fabric, Spark, and Python to automate complex workflows. - Lead engineering efforts on machine learning and artificial intelligence projects. - Develop and maintain robust web APIs (SOAP, REST) to support seamless data integration across internal and external systems. - Collaborate with external vendors to ensure the integrity and functionality of integrations. - Monitor and troubleshoot data processes, ensuring high availability and minimal downtime. - Proactively identify and resolve bottlenecks or inefficiencies in data pipelines and integrations. - Manage and prioritize work using the ticketing system while maintaining regular communication in stand-ups and stakeholder meetings. - Conduct code reviews to ensure adherence to best practices and high-quality deliverables. - Contribute to technical documentation for processes, tools, and workflows. - Provide tier 3 support to end users of integrated systems such as reporting and accounting. - Partner with business owners to identify areas for improvement and gather requirements. - Mentor other team members by sharing knowledge, conducting training sessions, and providing guidance on best practices. - Take ownership of complex projects, ensuring timely delivery and alignment with business objectives. Qualifications - Bachelor’s degree in Computer Science, Information Systems /other relevant degree or equivalent professional experience. - Expert knowledge of relevant languages, such as SQL, dbt, Python, and/or C#. - Expert knowledge of at least one data pipeline orchestration tool, such as Azure Data Factory. - Expert understanding of data modeling and ETL concepts. - Experience with version control systems (e.g., Git) and best practices. - Strong problem-solving skills and the ability to work independently on complex tasks. Typical Behaviors & Working Style - Versatile and adaptable, flexing to meet the needs of the situation. - Maintains people-orientation, even if reserved in nature. Must be helpful and service-oriented, with a strong focus on repeatable, high-quality results. - Decision-making is collaborative, but meticulous, requiring consideration of facts, established procedures, and proven processes. - Communicates based on the task or technical needs at hand, defining clear team roles. - Leads according to specialty or expertise. Will act with conviction to ensure quality standards, rarely delegating. Preferred Working Environment & Job Characteristics - A complex, senior-level data engineering environment with high expectations for independence and ownership. - A high-standards, reliability-focused setting where availability, performance, and data integrity are critical. - A fast-paced, multi-priority workload balancing delivery, operational support, and cross-team collaboration. What success in this role looks like - Data pipelines and integrations operate reliably at scale, supporting business-critical systems and analytics. - Operational issues are resolved proactively, with minimal downtime and continuous improvement. - The data engineering function grows stronger over time, through high-quality delivery, documentation, and ownership of complex initiatives. Working Conditions This is a work from home position. All technology required will be provided. Training - Orientation via some live remote and some pre-recorded video sessions. - IT security training. - Internal development process and procedures. - Company-approved AI technology. Salary $120,000 - $150,000 actual compensation within this range will be determined by multiple factors including candidate experience and expertise.