Job
Description
Software Engineer II (Data) Were looking for experienced Software Engineers to play a key role in delivering impactful software products as part of a high-performing Data Engineering team. Youll collaborate with Product Managers, UX Designers, Architects, and Engineers to modernize and build products aligned with the team's strategic goals. Engineering efforts leverage multi-cloud platforms, human-centered design, Agile, and DevOps practices to deliver robust, scalable, and high-quality solutions at a rapid pace. Youll be part of a team that values inclusivity, autonomy, collaboration, attention to detail, and the ability to navigate ambiguity and thrive in a dynamic environment. Team Overview The Data and AI enablement teams focus on accelerating end-to-end data and AI adoption across Advanced Analytics, Product teams, and Business Operations. The team drives data strategy, availability, and adoption through self-service tools such as AI model registry, model activation frameworks, multichannel testing platforms, and customer relationship analytics — all developed under a strong Responsible AI framework. The Business Data Domain team, part of the broader Data & AI organization, works at the intersection of modern data engineering and business enablement. The team partners across internal functions to architect new solutions, modernize legacy systems, adopt engineering best practices, and foster a culture of inclusive collaboration. Key Responsibilities Contribute to the delivery of complex technical solutions by breaking down large problems into smaller, actionable components Participate actively in team planning and sprint activities Ensure quality and integrity of the SDLC through usage of appropriate tools and best practices Independently triage complex technical issues Guide delivery of business needs spanning multiple applications Promote agile development practices and mentor peers on collaborative teamwork Coach new hires and junior engineers to enhance their impact and efficiency Contribute to internal tools and library development Understand and align technical work with business needs Communicate status, challenges, and solutions proactively Identify and mitigate risks in both individual and team work Collaborate across teams to devise innovative solutions for customer problems Stay committed to critical delivery timelines Basic Qualifications 3+ years of relevant professional experience with a degree or equivalent 1+ years of experience designing data lakes and reviewing code for design compliance 1+ years of experience tuning data models for performance and data quality 1+ years of hands-on experience with multiple programming languages 1+ years of experience in cloud computing services (e.g., AWS, Google Cloud) 1+ years of practitioner-level experience in large-scale data warehouse and BI tool migrations to cloud-based data lakes (e.g., Google Cloud) Preferred Qualifications Experience in omni-channel environments or high-scale systems Passion for exploring and adopting new technologies and trends Proactive learning and knowledge-sharing attitude Ability to effectively balance technical depth with business goals Technical Skills (Tools, Technologies, Frameworks, Platforms) Programming Languages: Multiple programming languages (unspecified, but likely includes Java, Python, etc.) Cloud Computing Services: AWS (Amazon Web Services) Google Cloud Platform (GCP) Data Engineering & Storage: Data lake design and development Cloud-based data lake migration (e.g., from traditional BI tools to Google Cloud Data Lake) Data modeling (performance tuning and data quality optimization) Big Data / Data Warehouse Tools: Large-scale data warehouse systems Business Intelligence (BI) tools (unspecified, but may include tools like BigQuery, Redshift, Snowflake, etc.) Self-Service Data Tools (implied exposure): AI model registry tools Multichannel testing frameworks Customer analytics platforms Applied Technical Skills (Practices, Design Principles, Methodologies, etc.) Data Engineering Practices: Designing scalable data lake architecture Data model optimization (performance, quality) Data quality engineering and best practices Data availability and consumption at scale Data enablement for AI/ML workflows Cloud-Native Engineering: Architecting solutions on cloud platforms (AWS/GCP) Migration from traditional BI to cloud-native infrastructure Understanding cost-efficient, scalable, and fault-tolerant cloud systems Software Development Practices: Full software development lifecycle (SDLC) understanding Code reviews, design compliance Agile development methods (Scrum, Kanban) DevOps mindset (although not tools-heavy in this JD) Problem Decomposition & Solution Design: Breaking down large business problems into deliverable technical components Multi-application solution planning and architecture alignment Collaboration & Team Practices: Working across functions with Product, UX, Architecture Mentoring and coaching junior developers Improving internal libraries, tools, and automation capabilities Communication & Risk Management: Proactive communication of blockers, risks, and progress Identifying and mitigating delivery or quality risks early AI/ML Enablement Support: Integrating AI models into data pipelines (via model registry and activation layers) Supporting Responsible AI development practices (ethical data usage, model monitoring)
