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 United States.
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.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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