Senior Manger, Data Engineer

Description

• Data pipelines & operations: Design, build, and run reliable, scalable, and cost-efficient data pipelines and datasets on Snowflake, meeting agreed SLAs and operational standards.
• Global Snowflake framework adoption: Implement and champion the Global standard Snowflake framework (standard layers, modelling approach, coding standards, testing, documentation, and promotion/release processes) and contribute improvements/reusable patterns back to the global community.
• CRM source integration: Ingest, harmonize, and model data from CRM and multiple platforms, including schema evolution handling, reconciliations, and consistent business definitions for downstream analytics.
• Data modelling & curation: Develop curated layers, data marts, and reusable data products with strong documentation, metadata, and lineage to enable self-service consumption and consistent reporting.
• Data quality & observability: Implement automated data validations, monitoring/alerting, and operational runbooks; perform root-cause analysis and drive sustainable fixes for incidents and data quality issues.
• Security, privacy & compliance-by-design: Apply least-privilege access (AWS IAM and Snowflake RBAC), encryption, secrets management, auditing, retention, and privacy controls (e.g., masking/row-level protections where applicable) in a regulated environment.
• Data science contribution (as needed): Support analytical problem framing, perform exploratory analysis and statistical evaluation, and develop/validate lightweight predictive models or prototypes as required, ensuring reproducibility and appropriate governance.
• ML/advanced analytics enablement: Create model-ready datasets and feature-oriented data products to support experimentation and scalable reuse, in collaboration with analytics/data science stakeholders.
• Cross-functional & global delivery: Translate business needs into technical requirements, communicate trade-offs clearly, and provide technical oversight (design/code reviews) for work delivered with the India-based development team.

Qualifications

Highest qualification: University

• Experience level: Typically, 8–12+ years of relevant experience in data engineering / analytics engineering, with end-to-end ownership of production data solutions (final requirement to be confirmed per internal levelling).
• AWS expertise: Strong hands-on experience with AWS data/pipeline services (commonly S3, Glue, Lambda, Step Functions, IAM, CloudWatch, and related services as needed).
• Snowflake expertise: Proven experience delivering solutions in Snowflake, including secure design, performance tuning, and cost optimization.
• Advanced SQL + Python: Expert SQL and strong Python skills for data engineering and analytics (automation, testing, maintainability).
• Framework-led delivery: Demonstrated ability to deliver within a standardized enterprise data framework (or evidence of driving standards adoption across teams).
• CRM data experience: Experience integrating and modelling Salesforce.com and/or Veeva CRM data, including handling frequent configuration/schema changes.
• Data science fundamentals: Working knowledge of statistics, exploration analysis, and ML concepts, with ability to contribute hands-on to analytical deliverables as needed.
• Bilingual requirement: Business-level Japanese and English are required, including the ability to lead technical discussions and write clear documentation for global stakeholders.