Data Analytics Engineer

About the Role

Key Responsibilities

• Embrace and drive a culture of accountability for customer and business outcomes by developing engineering solutions that solve complex problems with valuable outcomes, ensuring high-quality, lean designs and implementations.
• Serve as the technical advocate for products, ensuring code integrity, feasibility, and alignment with business and customer goals, including leading requirement analysis, component design, development, unit testing, integrations, and support.
• Maintain accountability for code design, implementation, quality, data, and ongoing maintenance and operations, creating technical specifications and writing high-quality, supportable, scalable code.
• Develop lean engineering solutions through rapid, inexpensive experimentation to solve customer needs, engaging with customers and product teams throughout the delivery process.
• Adopt a mindset favoring action and evidence over extensive planning, utilizing a learning-forward approach to deliver lean, supportable, and maintainable solutions.
• Collaborate with cross-functional teams including product management, experience, and delivery, integrating diverse perspectives to make well-informed decisions.
• Possess deep expertise in modern software engineering practices, including Agile methodologies and DevSecOps, to deliver daily product deployments with full automation and quality checks.
• Quickly acquire domain-specific knowledge, translating business needs and data designs into technical specifications and code.
• Exhibit exceptional communication skills to articulate complex technical concepts clearly, influence teammates, and create narratives aligning technical solutions with business objectives.
• Engage and collaborate with product engineering teams and customers, fostering relationships and driving consensus to create feasible solutions.

Requirements

• Bachelor's degree or equivalent in computer science, software engineering, or a related discipline.
• Minimum 2 years of experience with data cataloging, metadata management, and lineage mapping tools (e.g., Collibra, Alation, Informatica, Microsoft Purview, Apache Atlas, or similar).
• Minimum 2 years of experience with cloud-native engineering, using FaaS/PaaS/micro-services on cloud hyper-scalers like Azure, AWS, and GCP.
• Strong data engineering foundation with understanding of OOD/OOP, data-structures, algorithms, and code instrumentation.
• Strong SQL skills and experience with ETL/ELT pipelines.
• Knowledge of methodologies & tools like XP, Lean, SAFe, DevSecOps, SRE, ADO, GitHub, SonarQube, etc.
• Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities.

Nice to Have

• An advanced degree (e.g., MS) is preferred.
• Experience in AI/ML and GenAI is highly desirable.

Qualifications

• Educational background in computer science, software engineering, or related fields as indicated in requirements.

Benefits & Perks

• Broad range of employee benefits, including professional development opportunities, mentorship, and a supportive work environment.
• Competitive compensation package with a range estimated between $66,200 - $135,800, including potential participation in a discretionary annual incentive program.