Senior Staff Software Engineer, Data

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Staff Software Engineer, Data based in Canada.

This is a senior technical leadership role focused on transforming a data engineering and analytics function into a modern, scalable, product-oriented Data Platform organization.

You will define the architecture, operating model, technical standards, and execution roadmap needed to deliver reliable, governed, self-service data across the organization.

The role combines deep hands-on engineering with strategic leadership, including system design, prototyping, production coding, architecture reviews, and technical mentoring.

You will modernize data infrastructure across batch, streaming, real-time analytics, semantic layers, and AI/ML enablement.

A major focus will be building AI-ready data capabilities, including agent-readable semantic layers, vector stores, retrieval systems, and RAG-ready architectures.

You will partner closely with engineering, product, analytics, business, and executive stakeholders to connect data strategy with measurable business outcomes.

Success means building a resilient and trusted platform while elevating engineering practices, team capabilities, governance, and the broader data-driven culture.

Accountabilities:

• Define and own the end-to-end architecture strategy for data, analytics, and the Data Platform.
• Design scalable batch, streaming, and real-time data systems supporting structured and unstructured data.
• Establish standards for data modeling, semantic layers, reporting, governance, lineage, metadata, and data quality.
• Lead architecture reviews, technical decision-making, and adoption of modern approaches such as lakehouse, data mesh, and real-time analytics.
• Design and prototype critical platform components while writing production-quality code for complex and high-impact areas.
• Review schemas, transformations, dashboards, analytics models, and technical implementations while troubleshooting performance and reliability issues.
• Build AI-ready data infrastructure, including vector stores, embedding pipelines, retrieval systems, and RAG-ready architectures with strong lineage, governance, security, and observability.
• Develop a “Data for Agents” strategy that provides semantic layers and metadata enabling LLMs and AI agents to navigate enterprise data accurately.
• Create curated data products and reusable APIs that make trusted datasets accessible to applications, analytics platforms, and AI agents.
• Enable self-service data access through standardized models, semantic layers, and reusable platform capabilities.
• Partner with AI, product, and engineering teams on training datasets, feature stores, production inference pipelines, and agentic ETL/ELT workflows.
• Ensure platform reliability, scalability, resilience, high availability, monitoring, and disaster recovery readiness.
• Partner with product, finance, business operations, and leadership teams to define analytics requirements and deliver trustworthy, performant insights.
• Establish data governance, privacy, compliance, role-based access controls, auditability, validation processes, and quality frameworks.
• Define SLAs and SLOs for data availability, freshness, and accuracy while establishing monitoring, alerting, and incident response processes.
• Optimize cloud costs, query performance, latency, concurrency, and capacity planning as data volumes grow.
• Mentor senior engineers, analytics engineers, and data scientists while partnering across product, ML, platform, and business teams.
• Translate business questions into scalable data solutions and influence roadmaps through strong data platform and analytics expertise.
• Serve as the senior technical authority for data and analytics while promoting pragmatic AI adoption and outcome-driven innovation.

Requirements:

• Advanced degree in Computer Science, Engineering, or a related field.
• 15+ years of experience in data engineering, analytics engineering, or data platform roles.
• Proven experience architecting large-scale data and analytics systems in cloud environments.
• Strong hands-on expertise with modern data stacks and cloud data services across AWS, Azure, or GCP.
• Deep knowledge of analytics data modeling, including dimensional modeling, star and snowflake schemas, Data Vault, and related approaches.
• Advanced SQL skills and proficiency in Python, Scala, or Java.
• Advanced expertise in semantic layers and dimensional modeling, including technologies such as dbt or Cube, with the ability to provide agent-readable data context.
• Expertise with real-time streaming frameworks such as Spark, Flink, or Beam, combined with a strong understanding of batch and real-time architectures.
• Experience building reporting and business intelligence solutions at scale using tools such as Looker, Tableau, or Power BI.
• Strong understanding of data governance, security, privacy, lineage, metadata, and access-control best practices.
• Ability to operate effectively at both deeply technical and executive levels, with strong communication, collaboration, and leadership skills.
• Experience supporting AI/ML pipelines and feature engineering is a plus.
• Familiarity with real-time analytics, event-driven architectures, semantic layers, metrics stores, experimentation platforms, or product analytics is a plus.
• Experience working in high-growth SaaS or data-intensive organizations is also advantageous.

Benefits:

• U.S. base salary range of $235,000-$285,000 USD, with actual compensation determined by experience, skills, location, and applicable local pay requirements.
• Equity and a variety of additional benefits.
• Health, dental, and vision coverage for employees and their families.
• Life insurance and mental wellness coverage.
• Fertility and growing family support.
• Flex Time Off in addition to company-paid holidays.
• Paid family leave, medical leave, and bereavement leave.
• Retirement savings plans.
• Allowance to customize your home work and technology setup.
• Annual professional development stipend.