Senior Staff Software Engineer (Data)

We are seeking a Data Engineering Architect to lead the transformation of our current data engineering and analytics function into a modern, scalable, product-oriented Data Platform organization

You will define the vision, architecture, operating model, and execution roadmap required to evolve from project-based data delivery to a platform that enables self-service, reliable, governed, and analytics-ready data across the company

You will modernize our data stack, establish platform standards, introduce best practices for reliability and governance, and enable teams across the business to build data products efficiently and safely

In addition to platform transformation, you will ensure the data ecosystem delivers high-quality analytics and actionable insights

You will define architecture across ingestion, processing, modeling, semantic layers, analytics, and AI/ML enablement, ensuring data is trustworthy, accessible, secure, and performant

You will work closely with engineering leadership, product teams, analytics, and executive stakeholders to align technology strategy with business outcomes, mentor engineers, and build a data-driven culture

Success in this role means not only delivering a modern platform, but also elevating the team’s capabilities, processes, and ways of working to operate as a true Data Platform organization

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- This is a deeply hands-on leadership role for a technical expert who actively designs systems, prototypes solutions, reviews code, and guides teams through complex challenges

10+ years in data engineering, analytics engineering, or data platform roles

Ability to operate at both executive and deeply technical levels

Advanced SQL skills and proficiency in Python, Scala, or Java

Strong hands-on experience with modern data stacks in cloud environments

Advanced degree in Computer Science, Engineering, or related field

Hands-on experience with AWS, Azure, or GCP data services

Proven experience architecting large-scale data and analytics systems

Experience with distributed processing frameworks (Spark, Flink, etc.)

Strong understanding of data governance and security best practices

Strong understanding of both batch and real-time architectures

Experience building reporting and BI solutions at scale

Experience with BI tools (e.g., Looker, Tableau, Power BI, etc.)

Advanced expertise in dimensional data modeling and semantic layers (e.g., dbt, Cube) to provide “agent-readable” context

Deep expertise in data modeling for analytics (dimensional, star/snowflake, Data Vault, etc.)

Experience implementing semantic layers or metrics stores

Experience supporting AI/ML pipelines and feature engineering

Background in high-growth SaaS or data-intensive organizations

Familiarity with real-time analytics and event-driven architectures

Experience with experimentation platforms or product analytics