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Data Optimization Jobs in California (NOW HIRING)

Mixpanel

Delivery Engineer II

San Francisco, CA · On-site +1

Leverage modern data architecture expertise to create scalable data governance practices and data trust for our customers, including data optimization and re-implementation projects * Successfully ...

Living Spaces - Corporate

SEO Analyst

La Mirada, CA · On-site

$81K - $108K/yr

These strategies will include data and technical analysis, on-page optimization, content gap analysis, internal linking, competitive research, and detailed reporting. The role requires strong ...

Living Spaces

SEO Analyst

La Mirada, CA · On-site

$81K - $108K/yr

These strategies will include data and technical analysis, on-page optimization, content gap analysis, internal linking, competitive research, and detailed reporting. The role requires strong ...

Living Spaces

SEO Analyst

La Mirada, CA · On-site

$81K - $108K/yr

These strategies will include data and technical analysis, on-page optimization, content gap analysis, internal linking, competitive research, and detailed reporting. The role requires strong ...

Apple

SEO Analyst

Culver City, CA · On-site

$112K - $171K/yr

If you're energized by the intersection of data, search science, and scale, this role is built for you. Description We are looking for a detail-oriented, data-driven SEO Analyst to power organic ...

Efficient Computer

Optimization Engineer

San Jose, CA · On-site

$130K - $200K/yr

... data to drive the next major computing revolution About the Role We are seeking Software ... Efficient's Optimization Engineers optimize and benchmark applications, libraries, and kernels for ...

Apple

SEO Analyst

Culver City, CA · On-site

If you're energized by the intersection of data, search science, and scale, this role is built for you. Description We are looking for a detail-oriented, data-driven SEO Analyst to power organic ...

K2 Space

Senior Application Data Engineer

Los Angeles, CA · On-site

$160K - $200K/yr

Lead data architecture designs, drive data optimization, ensure data integrity and accuracy. * Evaluate and make decisions regarding competing data design tradeoffs. * Advocate, defend and convert ...

K2 Space

Senior Application Data Engineer

Los Angeles, CA · On-site

$160K - $200K/yr

Lead data architecture designs, drive data optimization, ensure data integrity and accuracy. * Evaluate and make decisions regarding competing data design tradeoffs. * Advocate, defend and convert ...

Diageo

Analyst Space Optimization

Aliso Viejo, CA · On-site

: Analyst Space Optimization Location: Southern CA About Diageo With over 200 brands sold in nearly ... Use various data sources including syndicated data, retailer POS, planogram data. * Assume ...

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Showing results 21-40

All JobsData Optimization JobsData Optimization Jobs in California

Data Optimization information

What is a data optimization?

A Data Optimization job involves improving the efficiency, accuracy, and accessibility of data within an organization. Professionals in this role analyze large datasets, refine data structures, and implement strategies to enhance data processing and storage. They work with data engineers, analysts, and business teams to ensure data supports performance goals and decision-making. Common tasks include cleaning data, reducing redundancies, and optimizing database queries.

What are the key skills and qualifications needed to thrive in data optimization, and why are they important?

To thrive in Data Optimization, you need strong analytical skills, expertise in data modeling, and a solid foundation in statistics or mathematics, usually supported by a relevant degree. Familiarity with tools such as SQL, Python, R, and data visualization platforms like Tableau, as well as certifications in data analytics or optimization software, is highly beneficial. Effective communication, problem-solving abilities, and a collaborative mindset are key soft skills for this role. These competencies are crucial for translating complex data into actionable insights that drive business efficiency and performance improvements.

What are some typical challenges faced in a data optimization role?

Professionals in Data Optimization often encounter challenges such as working with incomplete or inconsistent datasets, integrating data from multiple sources, and ensuring data quality and accuracy throughout the optimization process. Balancing technical efficiency with business objectives and communicating complex analytical findings in easily understandable ways can also be demanding. Collaboration with cross-functional teams is frequent, requiring both strong technical and interpersonal skills. Overcoming these challenges helps ensure that optimization projects deliver meaningful value and measurable impact for the organization.

What are the most commonly searched types of Data Optimization jobs in California?

The most popular types of Data Optimization jobs in California are:

What are popular job titles related to Data Optimization jobs in California?

For Data Optimization jobs in California, the most frequently searched job titles are:

What job categories do people searching Data Optimization jobs in California look for?

The top searched job categories for Data Optimization jobs in California are:

What cities in California are hiring for Data Optimization jobs?

Cities in California with the most Data Optimization job openings:

Data Optimization jobs near you
Infographic showing various Data Optimization job openings in California as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 13% Part Time, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Senior Data Analyst - Business Insights & Operations

Spinwheel

Oakland, CA • On-site, Remote

$98K - $124K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 15 days ago

Job description

About the Role
We're hiring a Senior Data Analyst to be the connective tissue between our data and every decision the company makes. Today, Finance and Revenue Operations each run their own reports, but those systems sync on a limited, manual cadence, and no one owns the full picture: Product and Leadership lack visibility into how the platform is actually being used, revenue reporting is fragmented across tools, and customer usage and health data is inconsistent and hard to trust.
This person will operationalize our data end-to-end, building the pipelines, dashboards, and automated reporting that let every function, including Product, Engineering, Data Engineering, Revenue, Finance, Customer Success, Operations, Support, GTM, and Leadership, see clearly and act quickly. This is not a role that duplicates the reporting Finance and Revenue Operations already own; it's the role that connects their outputs to everyone else, fills the gaps between systems, and builds the shared source of truth the company is missing. You'll report directly to our COO and partner with every function in the business, including Data Engineering on the systems and tooling that make reporting possible in the first place.
As a remote-first company, we welcome applicants based anywhere in the United States or Canada. You will join a high-ownership, high-trust engineering culture where self-starters thrive, ruthless prioritization is celebrated, and autonomy is the natural state of work. We move quickly, operate transparently, and expect every team member to think strategically, execute pragmatically, and elevate the people around them.
At Spinwheel, our mission is bold: we're building the infrastructure that powers intelligent debt management and financial empowerment. Our platform helps consumers understand, manage, and eliminate debt faster, powered by real-time credit data, optimization engines, AI-driven workflows, and modern financial APIs. Following our recent Series A, we're scaling rapidly and investing deeply in the data, reporting, and analytics infrastructure that lets us understand and run the business as well as we run the platform itself.
A key differentiator for this role: you will use AI tools and agents, including Claude, as a core part of how you work, automating recurring analysis, building lightweight agents that answer business questions on demand, and scaling reporting without scaling headcount.
Key Responsibilities
Building the Company's Shared Source of Truth
  • Design and maintain unified reporting that pulls from multiple systems (CRM, Accounting, Invoicing, Operational DBs, etc.) for one consistent, trusted view.
  • Replace ad hoc, manually-synced exports with reliable, more real-time data pipelines and refresh cadences.
  • Define and document shared metric definitions (e.g., MRR, activation, churn) so every function reports the same numbers the same way.
Data Engineering Partnership & Stack Strategy
  • Work closely with Data Engineering on the architecture, reliability, and scalability of the pipelines feeding Redshift and MongoDB Atlas, flagging gaps that affect reporting quality or freshness.
  • Serve as a key voice in evaluating and selecting BI and analytics tooling, including assessing whether our current tool, ThoughtSpot, still meets the business's needs as we scale, and helping lead a transition if a change is warranted.
  • Represent the reporting and analytics perspective in broader data stack decisions, including warehouse, ELT/ETL, data catalog, and governance, so tooling choices serve the people using the data, not just the people moving it.
Revenue & Financial Visibility
This complements, not replaces, the revenue and pipeline reporting Finance and Revenue Operations already own. The goal is to make their numbers visible, consistent, and easy to act on everywhere else in the business.
  • Partner with Finance to extend revenue reporting (MRR/ARR bridge, new vs. expansion vs. churned revenue, revenue by segment and product) into the shared dashboards other functions rely on.
  • Help surface collections and billing risk (AR aging, failed payments) and support reconciliation between bookings, invoiced, and collected revenue, working alongside Finance.
  • Build cohort and retention analyses (gross and net revenue retention) that build on Finance and RevOps reporting to explain the drivers behind revenue movement for the rest of the business.
Product & Platform Usage Insight
  • Query MongoDB Atlas (our platform's operational database) directly, using the aggregation framework, to build adoption, activation, and engagement dashboards so Product and Leadership can see how the platform is actually being used.
  • Track API and feature usage by customer and endpoint, and connect usage patterns to retention and expansion.
  • Measure the impact of new releases and features on usage and adoption metrics.
Customer Usage & Account Health
  • Build a composite customer health score combining usage, support activity, and sentiment.
  • Flag renewal risk and expansion opportunity for Customer Success and Sales before it becomes a surprise.
  • Bring rigor to customer usage reporting so it's a reliable input to CS and GTM decisions, not an afterthought.
GTM, Sales & Marketing Reporting
  • Partner with Revenue Operations (not duplicate it) to connect pipeline, funnel, and campaign data to the rest of the business's metrics.
  • Support win/loss, attribution, and sales-cycle analysis where it intersects with revenue and product usage.
Executive & Cross-Functional Reporting
  • Own the recurring business review package that gives Leadership one place to see the whole business.
  • Support board reporting and OKR/goal tracking with consistent, defensible numbers.
  • Be a thought partner to every function leader, translating their questions into analysis rather than just tickets.
AI-Enabled Reporting & Automation
This is what separates the role from a traditional analyst seat: AI isn't a side tool here, it's part of how the reporting pipeline itself gets built and run.
  • Build a Claude-powered natural-language query agent that turns a plain-English question ("which customers' API usage dropped last month?") into a MongoDB aggregation pipeline or a Redshift SQL query and returns a vetted answer, so Product, CS, and Leadership can self-serve instead of filing a request with Data.
  • Build automated anomaly detection and alerting that continuously watches key metrics (MRR, API call volume, activation, churn signals) and proactively posts to Slack when something looks off.
  • Use AI to draft a first-pass "what happened and why" written summary for each business review, which you then review and sharpen.
  • Use AI to spot and reconcile mismatches across systems, such as the same customer represented differently in HubSpot, QuickBooks, and MongoDB Atlas.
  • Use AI to mine call transcripts (Grain and Granola) and support tickets for recurring themes, objections, and risk signals, and fold that into the customer health score.
  • Package these agents and workflows as reusable internal tools so other functions can extend or reuse them, rather than treating every new question as a one-off analysis request.
What Success Looks Like
First 90 Days
  • Map every system of record (HubSpot, QuickBooks, MongoDB Atlas, Jira, Freshdesk, etc.), how they connect today, and where the gaps are.
  • Identify the top three to five highest-value reporting gaps (revenue, product usage, customer health) and have a plan to close them.
First 6 Months
  • Product usage and customer health reporting are trusted and used regularly by Product and CS leadership.
  • Revenue reporting is timely, reconciled, and no longer dependent on manual, infrequent exports.
  • At least one AI-driven reporting agent or automated workflow is live and adopted by a function outside of Data.
Required Qualifications
  • 5+ years in data or business analytics, analytics engineering, or a similar cross-functional analytics role.
  • Strong SQL and hands-on experience with a cloud data warehouse (ideally Redshift) and a BI/visualization tool (we currently use ThoughtSpot; experience with Looker, Tableau, Mode, Hex, or Power BI also welcome).
  • Demonstrated use of AI tools (Claude, ChatGPT, or similar) to automate analysis, build agents, or accelerate reporting workflows.
  • Experience building data models or a semantic layer that multiple teams can report from consistently.
  • Comfort connecting and reconciling data across disparate systems (CRM, billing/ERP, product analytics, support/ticketing platforms like Freshdesk).
  • Excellent stakeholder skills, able to work equally well with Engineering, Data Engineering, Finance, Sales, CS, and Leadership, and to push back productively when a request needs sharpening.
Preferred Qualifications
  • Hands-on experience querying NoSQL/document databases, ideally MongoDB and its aggregation framework, a plus given our platform's operational data lives in MongoDB Atlas.
  • Experience in fintech, SaaS, or a usage/subscription-based business model.
  • Comfortable using AI coding tools (e.g., Claude Code) to build and maintain automation pipelines, with enough Python/SQL literacy to review, debug, and validate the code they generate.
  • Experience partnering with (not replacing) existing Finance and Revenue Operations analytics functions.
  • Experience evaluating, selecting, or migrating BI/analytics tooling (e.g., ThoughtSpot, Looker, Tableau, Mode, Hex, Power BI) as a business scales.
What We Offer
  • 100% remote-first culture (work anywhere in the US or Canada)
  • Competitive salary and equity opportunities
  • Unlimited PTO, flexible schedule, and paid holidays
  • Comprehensive health, dental, and vision insurance
  • A culture built on ownership, transparency, autonomy, and craftsmanship
  • Semi-annual company retreats to destinations like Costa Rica, Banff, Chicago, Mexico City, and more
  • Real opportunities to shape how the whole company sees and uses its data

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