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Mode Analytics Jobs in California (NOW HIRING)

Specter

Fleet Reliability Engineer

San Francisco, CA · On-site

$120K - $152K/yr

Build the failure-mode analysis that tells engineering what to fix at the source. * Own fleet-wide trend and forecasting work, including power and solar planning. Reliability Economics ...

Specter

Fleet Reliability Engineer

San Francisco, CA · On-site

$120 - $160/hr

Build the failure-mode analysis that tells engineering what to fix at the source. * Own fleet-wide trend and forecasting work, including power and solar planning. Reliability Economics ...

Hinge Health, Inc

Senior Financial Analyst I

San Francisco, CA · On-site

$100K - $151K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Prior experience with SFDC, Adaptive Planning, Mode Analytics, and/or SQL. * High interpersonal intelligence with an aptitude for listening, building relationships, and influencing stakeholders.

EV Mode

Sales Administrator

Irvine, CA · On-site

$20 - $23/hr

EV Mode, LLC Location: Irvine, California Website: evmode.com Type: Full-Time About EV Mode, LLC EV ... Reporting & Analytics : Generate sales reports, track key performance metrics, and provide insights ...

Twitch

Senior Accountant, Content Accounting

San Francisco, CA

$87K - $109K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Experience with SAP, QuickSight, Mode Analytics, or other related ERP, BI, or data visualization tools. * Hands on experience with emerging AI technologies (e.g., Claude Code, MCP and Agentic ...

Twitch

Senior Accountant, Content Accounting

Los Angeles, CA

$79K - $100K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Experience with SAP, QuickSight, Mode Analytics, or other related ERP, BI, or data visualization tools. * Hands on experience with emerging AI technologies (e.g., Claude Code, MCP and Agentic ...

Amazon

Senior Accountant, Content Accounting

San Francisco, CA · On-site

$87K - $109K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... Mode Analytics, or other related ERP, BI, or data visualization tools. - Hands on experience with emerging AI technologies (e.g., Claude Code, MCP and Agentic Workflows) - Avid Twitch user as a ...

Apple

AR/VR Design Validation Engineer

Bodega Bay, CA

$184K - $324K/yr

  • Medical

  • Dental

  • Retirement

This role balances optical simulation pipeline development with hands-on responsibility for defining engineering builds, designing experiments, conducting failure mode analysis, and translating ...

Twitch

Senior Accountant, Content Accounting

San Francisco, CA · On-site

$87K - $109K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with SAP, QuickSight, Mode Analytics, or other related ERP, BI, or data visualization tools. * Hands on experience with emerging AI technologies (e.g., Claude Code, MCP and Agentic ...

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Showing results 1-20

All JobsMode Analytics JobsMode Analytics Jobs in California

Mode Analytics information

See California salary details

$59.7K

$122.2K

$172.7K

How much do mode analytics jobs pay per year?

As of Aug 15, 2026, the average yearly pay for mode analytics in California is $122,227.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,700.00 and $151,500.00 per year, depending on experience, location, and employer.

How does a Mode Analytics professional typically collaborate with cross-functional teams within an organization?

As a Mode Analytics professional, you will frequently work alongside data engineers, product managers, and business stakeholders to deliver actionable insights. Collaboration often involves gathering requirements, interpreting business needs, and presenting findings through data visualizations or dashboards. Regular communication and the ability to translate technical data into meaningful recommendations are key to ensuring your analyses effectively support decision-making across departments. This role thrives on teamwork, as projects usually span multiple business units, requiring both technical expertise and strong interpersonal skills.

What is Mode Analytics?

Mode Analytics is a collaborative data analytics platform designed for data scientists and analysts. It enables users to explore, analyze, and visualize data using SQL, Python, and R, all within a single environment. Mode makes it easy to share interactive reports and dashboards with teams, streamlining the process of turning data insights into business decisions. Its integrations and automation features help organizations manage complex data workflows efficiently.

What is the difference between Mode Analytics vs Data Analyst?

AspectMode AnalyticsData Analyst
Primary RoleData exploration, visualization, and reporting using Mode platformAnalyzing data, generating reports, and providing insights across various tools
Required SkillsSQL, Python, data visualization, familiarity with Mode platformSQL, Excel, data visualization, statistical analysis
Work EnvironmentData teams, analytics departments, tech companiesBusiness units, consulting firms, finance, marketing
CertificationsNone specific, familiarity with data toolsSQL certifications, data analysis courses

Mode Analytics professionals focus on using the Mode platform for data visualization and reporting, often working closely with data teams. Data Analysts perform broader data analysis tasks across various tools and industries. While both roles require SQL and data visualization skills, Mode Analytics specialists are more platform-specific, whereas Data Analysts have a wider range of tools and techniques.

What are the key skills and qualifications needed to thrive as a Mode Analytics professional, and why are they important?

To thrive as a Mode Analytics professional, you need strong analytical skills, a solid understanding of SQL and data visualization, and experience with data analysis or business intelligence, often supported by a relevant degree. Familiarity with tools such as Mode Analytics, Python or R for data manipulation, and cloud-based data warehouses is typically required. Excellent problem-solving, communication, and collaboration skills help translate data insights into actionable business strategies. These skills are crucial for effectively leveraging data to drive decision-making and deliver impactful results for organizations.

What are popular job titles related to Mode Analytics jobs in California?

For Mode Analytics jobs in California, the most frequently searched job titles are:

What job categories do people searching Mode Analytics jobs in California look for?

The top searched job categories for Mode Analytics jobs in California are:

Mode Analytics jobs near you
Infographic showing various Mode Analytics job openings in California as of August 2026, with employment types broken down into 1% Internship, 89% Full Time, 5% Part Time, 2% Temporary, and 3% Contract. Highlights an 78% Physical, 7% Hybrid, and 15% Remote job distribution, with an average salary of $122,227 per year, or $58.8 per hour.

Fleet Reliability Engineer

Specter

San Francisco, CA • On-site

$120K - $152K/yr

Full-time

Re-posted 2 days ago

Job description

Company BackgroundSpecter's mission is to help automate the physical world.
Today, we build video sensors with state-of-the-art AI agents that answer any question, anywhere in their environments. Our systems can automatically detect and reason about any physical activity captured on camera, from security incidents (e.g. perimeter intrusion, theft, LPR), to safety monitoring (e.g. PPE detection, injured people), to operational efficiency (e.g. material tracking, congestion monitoring). We offer both long range wireless (1km range) and wired sensor variants to suit any deployment.
Our co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.
The RoleWe're hiring a Fleet Reliability Engineer to keep our sensor fleet running in the field by building the data, analytics, and recovery mechanisms that prevent failures from becoming incidents. As we scale toward thousands of sensors, fleet health becomes a data-and-systems problem.
This is the proactive, highest-leverage side of reliability: own the telemetry and data pipeline, verify that fixes hold fleet-wide, and turn field signal into cost-weighted decisions about what to fix first. Much of today's operational load is addressable through better instrumentation, alert hygiene, and recovery verification, at little to no field cost.
Responsibilities:
Reliability Data Platform - Primary
  • Own the fleet's reliability data pipeline end to end: telemetry aggregation, storage, and instrumentation.
  • Drive down observability cost - own the tooling spend and cut what we pay for but don't use.
  • Instrument the fleet and own the health metrics that measure reliability.

Proof-of-Recovery & Alert Hygiene
  • Verify that fixes hold fleet-wide, not just on the device that paged.
  • Cut alert noise at the source - separate real failures from self-resolving ones.
  • Turn repeat failure patterns into automated detection and recovery.

Fleet Health & Failure-Mode Analytics
  • Turn fleet telemetry into a live picture of which cohorts, hardware revisions, and firmware versions are trending toward failure, and why.
  • Build the failure-mode analysis that tells engineering what to fix at the source.
  • Own fleet-wide trend and forecasting work, including power and solar planning.

Reliability Economics & Prioritization
  • Score reliability work in dollars - field-trip cost, hardware-return cost, observability spend - and prioritize the most expensive problems first.
  • Set and track the fleet's reliability targets: uptime, offline rate, truck-rolls per sensor-year.
  • Give the team the data to make reliability-versus-cost tradeoffs.
Qualifications:
  • Strong data and software skills - Python (or Go) and SQL - and the ability to own a data pipeline end to end.
  • Hands-on building and tuning observability stacks (OpenTelemetry, Grafana, Prometheus, Datadog, or similar), including their cost.
  • Experience operating physical or embedded device fleets at scale, and reasoning about how hardware fails in the field.
  • Comfortable turning messy field telemetry into trends, failure modes, and forecasts.
  • Fluency with databases and data modeling (PostgreSQL or equivalent); infrastructure-as-code familiarity (Terraform or similar) a plus.
  • Bias toward building mechanisms over doing manual work.
  • Nice to have: reliability/SRE fundamentals (SLOs, error budgets, proof-of-recovery) applied to a physical fleet.
  • Nice to have: experience across the hardware-software boundary - power, connectivity, and physical failure modes.

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