Senior Data Engineer

About the role

The Senior Data Engineer bridges business objectives and technical execution, leading data engineering initiatives covering data lakes, data platform modernization and reporting solutions. The role calls for strong cloud data architecture expertise, hands-on delivery experience and effective collaboration with business and technology stakeholders. It is a hybrid position.

Key responsibilities

• Partner with business unit leaders, product owners and IT stakeholders to capture requirements, define project scope and set delivery timelines.
• Estimate workload and effort, and break down complex initiatives into technical epics, user stories and execution roadmaps.
• Drive delivery from architecture design through testing, deployment and handover, managing risks, dependencies and scope creep.
• Act as the main technical link between business stakeholders and the data engineering team.
• Design, build and optimize scalable batch and real-time pipelines, data lakehouse architectures and data warehousing solutions.
• Provide technical leadership for enterprise data platforms, including architecture, best practices and optimization on Azure Cloud, Databricks (Spark), DataWorks, MaxCompute, OSS and Hologres.
• Implement data orchestration, ingestion, cleansing, transformation, augmentation and data quality controls, and enforce quality standards and data governance.
• Conduct code reviews, troubleshoot technically and keep pipelines secure, resilient and cost-optimized.
• Mentor and coach mid- and junior data engineers.

Required profile

• Bachelor's or Master's degree in Computer Science, Information Systems, Quantitative Engineering or a related field.
• Minimum 10+ years of IT experience, with at least 3+ years in data engineering delivery leadership or technical lead roles.
• Solid understanding of insurance or financial services domains such as policy administration, claims management, actuarial metrics and agency analytics.
• Proven delivery leadership, including effort estimation, scope decomposition, sprint planning and managing stakeholder expectations.
• Excellent verbal and written communication, able to explain technical visions and trade-offs to non-technical audiences.

Required skills

• Azure Cloud Services (Azure Data Lake Storage, Azure Data Factory, Azure SQL/Synapse) and databases (Oracle, SQL Server or PostgreSQL).
• Azure Databricks, Apache Spark (PySpark / Spark SQL) and modern ETL/ELT pipeline design.
• Dimensional modeling (Kimball), data lakehouse architecture and data warehousing.
• Familiarity with AliCloud tools (DataWorks, MaxCompute, OSS, Hologres).
• Experience with FineBI, Power BI or similar reporting tools.