Original listing text, shown exactly as published by the company.
What you’ll do
Architecture & Technical Leadership
- Define and own the end-to-end data and analytics architecture strategy
- Design scalable batch, streaming, and real-time data systems
- Establish standards for data modeling, semantic layers, and reporting
- Lead architecture reviews and technical decision-making
- Drive adoption of modern architectures (lakehouse, data mesh, real-time analytics)
Hands-On Engineering
- Design and prototype critical data platform components
- Write production-quality code for complex or high-impact areas
- Review schemas, transformations, dashboards, and analytics models
- Troubleshoot performance and reliability issues across pipelines and queries
- Optimize workloads for latency, concurrency, and cost
Data Platform & Pipeline Ownership
- Design and architect a scalable data platform supporting ingestion, transformation, and delivery of both structured and unstructured data across batch and real-time pipelines.
- Design a "Data for Agents" strategy, ensuring our data warehouse is structured with the semantic layers and metadata necessary for LLMs to navigate it accurately.
- Build AI-ready data infrastructure, including vector stores, embedding pipelines, and retrieval systems that power LLM and agentic workflows.
- Develop a RAG-ready data architecture that enables trusted enterprise data retrieval with strong lineage, governance, security, and observability.
- Create curated data products and reusable APIs that make high-quality datasets easily consumable by applications, analytics platforms, and AI agents.
- Enable self-service data access for engineering, analytics, and business teams through standardized models, semantic layers, and platform capabilities.
- Partner with AI, product, and engineering teams to support training datasets, feature stores, and production AI inference pipelines.
- Build agentic ETL/ELT pipelines that use AI agents to autonomously discover sources and generate transformations.
- Ensure reliability, scalability, and resilience of the platform, including high availability, monitoring, and disaster recovery readiness.
Analytics & Business Intelligence
- Partner with product, finance, business operations, and leadership teams to define analytics needs
- Design scalable data models for reporting and advanced analytics
- Ensure analytics solutions are performant, trustworthy, and easy to use
- Drive adoption of data-driven culture through reliable insights
Governance, Quality & Security
- Define data governance, lineage, cataloging, and metadata standards
- Establish data quality frameworks and validation processes
- Ensure privacy, compliance, and secure access to sensitive data
- Implement role-based access controls and auditability
Leadership & Collaboration
- Mentor senior engineers, analytics engineers, and data scientists
- Partner with product, ML, platform, and business teams
- Translate business questions into scalable data solutions
- Influence roadmaps using data platform and analytics considerations
- Act as the executive technical authority for data and analytics
Operational Excellence
- Define SLAs/SLOs for data availability, freshness, and accuracy
- Establish monitoring, alerting, and incident response processes
- Optimize cloud costs and query performance
- Support capacity planning for data growth
Culture & Enablement
- Be an evangelist for pragmatic AI adoption.
- Help establish a culture of outcome-driven innovation.
Qualifications
Required
- Advanced degree in Computer Science, Engineering, or related field
- 10+ years in data engineering, analytics engineering, or data platform roles
- Proven experience architecting large-scale data and analytics systems
- Strong hands-on experience with modern data stacks in cloud environments
- Deep expertise in data modeling for analytics (dimensional, star/snowflake, Data Vault, etc.)
- Advanced SQL skills and proficiency in Python, Scala, or Java
- Advanced expertise in dimensional data modeling and semantic layers (e.g., dbt, Cube) to provide "agent-readable" context.
- Experience with distributed processing frameworks (Spark, Flink, etc.)
- Experience building reporting and BI solutions at scale
- Strong understanding of both batch and real-time architectures
- Hands-on experience with AWS, Azure, or GCP data services
- Experience with BI tools (e.g., Looker, Tableau, Power BI, etc.)
- Strong understanding of data governance and security best practices
- Ability to operate at both executive and deeply technical levels
Nice to Have
- Experience supporting AI/ML pipelines and feature engineering
- Familiarity with real-time analytics and event-driven architectures
- Experience implementing semantic layers or metrics stores
- Background in high-growth SaaS or data-intensive organizations
- Experience with experimentation platforms or product analytics
CompensationCompensation for this position includes a base salary, equity and a variety of benefits. The U.S. base salary range for this role is $235,000 - $285,000 USD. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable.
