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)
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
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.
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
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
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
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
Be an evangelist for pragmatic AI adoption.
Help establish a culture of outcome-driven innovation.

Requirements

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.

Benefits

Health, dental, and vision care for you and your family
Life insurance
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 policies
Retirement saving plans
Allowance to customize your work and technology setup at home
Annual professional development stipend

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data architecturedata modelingSQLPythonScalaJavadimensional data modelingdata governanceETLreal-time analytics

Soft Skills

leadershipmentoringcommunicationcollaborationinfluencingproblem-solvinginnovationtechnical decision-makingdata-driven culture advocacycapacity planning

Certifications

advanced degree in Computer Scienceadvanced degree in Engineering