Senior Staff Software Engineer, Data

Accountabilities:: Define and own the end-to-end architecture strategy for data and analytics, including scalable batch, streaming, and real-time systems. Lead the transformation toward a modern Data Platform using approaches such as lakehouse, data mesh, real-time analytics, self-service data access, and reusable data products. Design and prototype critical platform components while writing production-quality code for complex and high-impact areas. Establish standards for data modeling, semantic layers, reporting, metadata, lineage, governance, and data quality. Build scalable infrastructure for structured and unstructured data, including ingestion, transformation, APIs, vector stores, embeddings, retrieval systems, and RAG-ready architectures. Develop "Data for Agents" capabilities that provide LLMs and AI agents with reliable, well-structured, governed, and discoverable enterprise data. Partner with AI, product, and engineering teams on training datasets, feature stores, production inference pipelines, and agentic ETL/ELT workflows. Ensure platform reliability, availability, observability, security, disaster recovery readiness, performance, and cost efficiency. Support analytics and business intelligence by developing scalable models and enabling trustworthy, performant, and accessible insights. Mentor senior engineers, analytics engineers, and data scientists while serving as a senior technical authority for data and analytics. Establish SLAs/SLOs, monitoring, alerting, incident response, capacity planning, and operational standards for the growing data ecosystem. Translate business needs into scalable technical solutions and influence product and engineering roadmaps through data expertise. Champion pragmatic AI adoption and outcome-driven innovation across the organization. Requirements: Advanced degree in Computer Science, Engineering, or a related field. 15+ years of experience in data engineering, analytics engineering, data platforms, or closely related roles. Proven experience architecting and scaling large data and analytics systems in cloud environments. Deep expertise in data modeling for analytics, including dimensional modeling, star/snowflake schemas, Data Vault, and semantic layers. Advanced SQL skills and strong proficiency in Python, Scala, or Java. Hands-on experience with modern data stacks and cloud data services across AWS, Azure, or GCP. Strong knowledge of batch, streaming, and real-time architectures, with expertise in frameworks such as Spark, Flink, or Beam. Experience designing semantic layers or metrics stores using technologies such as dbt or Cube, particularly for making data accessible to AI and agentic systems. Experience building reporting and business intelligence solutions at scale, with familiarity with tools such as Looker, Tableau, or Power BI. Strong understanding of data governance, lineage, metadata, privacy, security, access controls, and auditability. Ability to operate effectively at both executive and deeply technical levels, with strong communication, collaboration, and technical leadership skills. Experience supporting AI/ML pipelines, feature engineering, real-time analytics, event-driven architectures, experimentation platforms, or product analytics is a plus. Experience in high-growth SaaS or data-intensive environments is also desirable. Benefits: Base salary, equity, and a comprehensive benefits package. U.S. base salary range of $235,000–$285,000 USD , with actual compensation determined by experience, skills, location, and applicable local requirements. Health, dental, and vision coverage for employees and their families. Life insurance and mental wellness coverage. Fertility and growing-family support. Flexible time off in addition to company-paid holidays. Paid family leave, medical leave, and bereavement leave. Retirement savings plans. Allowance to customize home-office and technology equipment. Annual professional development stipend. Remote work environment.