Job Description
This job was selected by the SpringCube team to help AI, Data and Cloud Engineers discover relevant San Francisco Bay Area employers. Sign up to view the full employer details and apply directly with the hiring company.
Company Overview
The company is a global leader in consumer cybersecurity, focused on protecting people rather than just devices. Its integrated products help families and communities stay safe online, with an eCommerce platform that serves millions of customers worldwide across purchasing, checkout, subscription, and auto-renewal journeys.
Responsibilities
Build the Data Foundation
- Develop production ETL/ELT pipelines that bring together data from eCommerce, MarTech, observability, and operational data stores.
- Transform raw data into analytics-ready and AI-ready datasets using advanced SQL, data modeling, and automated quality validation.
- Design dimensional data models, including star and snowflake schemas, with appropriate grain, partitioning, and performance considerations.
- Build gold-layer summarized datasets that consolidate large volumes of raw events into reliable entity-level daily records.
- Establish data foundations that support analytics, KPI reporting, executive dashboards, and natural-language querying.
Develop eCommerce Operational Intelligence
- Design and deliver an eCommerce Health view that combines system health signals with commercial performance indicators.
- Integrate technical metrics such as latency, errors, integrations, data-flow health, delivery velocity, quality, security posture, incident trends, and service ownership.
- Combine these signals with commercial metrics such as conversion, checkout performance, auto-renewal, and feature adoption.
- Create a trusted source of operational intelligence for engineering and business stakeholders.
Build AI-Powered Engineering Solutions
- Develop AI agents that identify anomalies, highlight critical issues, provide actionable context, notify appropriate stakeholders, and support intelligent incident triage.
- Build AI productivity tools that assist engineering teams with root-cause analysis, intelligent search across telemetry and code, and runbook assistance.
- Own the lifecycle of LLM-powered features, including prompt design, retrieval and grounding, tool use, guardrails, evaluation, and operational reliability.
- Identify opportunities to apply AI across engineering workflows and help teams adopt practical AI solutions.
Build Applications and Internal Tools
- Use Databricks as an application and product platform in addition to its data-processing capabilities.
- Leverage technologies such as Databricks Genie, Databricks Apps, and Lakebase to develop internal applications, chatbots, and operational tools.
- Deliver applications that stakeholders can directly interact with rather than limiting output to datasets and pipelines.
- Partner with Product, SRE, Security, and Architecture teams to translate operational challenges into solutions that teams can adopt.
- Help identify and expand effective AI use cases across engineering organizations.
What You’ll Bring
Experience
- 8+ years of experience in application development, including designing and architecting large-scale production applications and distributed systems.
- 5+ years of hands-on production data engineering experience developing pipelines, ETL/ELT workflows, and analytics platforms.
- Demonstrated expertise working with business-critical data and applying data engineering skills across different domains and platforms.
Disclaimer
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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
2. No Client Relationship: This company is not a client of SpringCube unless stated.
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