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Data Optimizer Jobs in California (NOW HIRING)

BayRock Labs

Principal Data Engineer

Milpitas, CA · On-site

... optimization strategies. Qualifications : Required : • 5-10+ years of hands‑on experience in data engineering or data architecture • Strong expertise in Azure + Databricks ecosystem • Proven ...

Bayrock Labs

Principal Data Engineer

Milpitas, CA · On-site

$80 - $85/hr

Performance tuning, cluster optimization * CI/CD for Databricks workloads Big Data & Processing Frameworks * Apache Spark with advanced PySpark transformations * Structured Streaming & batch data ...

Cogitativo

Senior Data Scientist

Berkeley, CA · On-site

Senior Data Scientist Senior Data Scientists on our team partner with product managers, SMEs and our clients to form a cross-functional team driving optimization of precious healthcare resources. We ...

Cogitativo

Senior Data Scientist

Berkeley, CA · On-site

Senior Data Scientist Senior Data Scientists on our team partner with product managers, SMEs and our clients to form a cross-functional team driving optimization of precious healthcare resources. We ...

Cogitativo

Senior Data Scientist

Berkeley, CA · On-site

Senior Data Scientist Senior Data Scientists on our team partner with product managers, SMEs and our clients to form a cross-functional team driving optimization of precious healthcare resources. We ...

GoFundMe

Staff Data Scientist

San Francisco, CA · On-site

Experience with marketing, growth, marketplace, and/or product optimization problems. * Strong ... Experience mentoring data scientists, analysts, or other technical team members. Preferred

DPP Tech, Inc.

Data Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Advanced SQL skills for complex queries and performance optimization. * Hands-on experience with DBT (Data Build Tool) for data transformation. * Experience building scalable ETL/ELT pipelines.

GoFundMe.com

Staff Data Scientist

San Francisco, CA · On-site

Experience with marketing, growth, marketplace, and/or product optimization problems. * Strong ... Experience mentoring data scientists, analysts, or other technical team members. Preferred

MOCSE FEDERAL CREDIT UNION

DATA ARCHITECT

Modesto, CA

$44.72 - $55.90/hr

Platform Optimization & Experimentation * Continuously optimize analytics platform performance and cost efficiency * Experiment with new analytical techniques, tools, and methodologies * Expand data ...

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Data Optimizer information

What is a data optimizer?

Data Optimizers are professionals who analyze, refine, and improve how data is collected, stored, processed, and utilized within an organization. They work to enhance data quality, streamline workflows, and ensure that businesses can make the most effective use of their data assets. By implementing best practices and leveraging technology, Data Optimizers help organizations gain actionable insights, reduce inefficiencies, and support better decision-making. Their role often involves working closely with data engineers, analysts, and business stakeholders to optimize data systems and processes.

How does a data optimizer typically collaborate with data engineers and analysts within a project team?

A Data Optimizer works closely with data engineers to ensure data pipelines are efficient, scalable, and aligned with organizational goals. They often review current data structures, identify inefficiencies, and propose enhancements that streamline data flow and improve performance. Collaboration with analysts is also key, as Data Optimizers help ensure data quality and availability for analytical tasks, often translating business needs into technical improvements. This role requires strong communication skills and the ability to bridge the gap between technical teams and business stakeholders.

What are the key skills and qualifications needed to thrive as a data optimizer, and why are they important?

To thrive as a Data Optimizer, you need strong analytical skills, a solid understanding of data management principles, and experience with data cleaning and transformation, typically supported by a degree in computer science, statistics, or a related field. Familiarity with tools like SQL, Python, data visualization software, and data warehousing systems is common, and certifications in data analytics or database management can be advantageous. Attention to detail, problem-solving abilities, and effective communication are crucial soft skills for interpreting data and collaborating with cross-functional teams. These competencies ensure data integrity, facilitate informed decision-making, and drive organizational efficiency through optimized data processes.

What is the difference between Data Optimizer vs Data Analyst?

AspectData OptimizerData Analyst
CredentialsTypically requires certifications in data management, SQL, or data analysis toolsOften holds degrees in statistics, mathematics, or related fields
Work EnvironmentFocuses on data quality, efficiency, and process improvement within data systemsAnalyzes data sets to identify trends and generate reports for decision-making
Employer & Industry UsageUsed in industries emphasizing data infrastructure and optimizationCommon across various industries for business insights and reporting

While both roles work with data, Data Optimizers focus on enhancing data quality and system efficiency, whereas Data Analysts interpret data to support business decisions. Understanding these differences helps organizations assign the right skills to each role.

What cities in California are hiring for Data Optimizer jobs?

Cities in California with the most Data Optimizer job openings:

Data Optimizer jobs near you
Infographic showing various Data Optimizer job openings in California as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Research Data Scientist, Brand Bidding Optimization, YouTube Ads

YouTube

Mountain View, CA • On-site

Full-time

Posted 25 days ago

Job description

Minimum qualifications:
  • Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  • 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
  • Experience in operations research, game theory, or machine learning.
  • Experience in digital advertising or brand advertising.

Preferred qualifications:
  • PhD degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.

About the job
Advertising serves as the economic backbone of the YouTube platform that engages billions of users globally through an ever-growing and diversifying content library. YouTube advertisers range from global and local businesses, trying to broaden their outreach among the internet audience, to those seeking immediate commercial engagement from interested users. The first group are referred to as Brand Advertisers, and they allocate their marketing budgets to a wide spectrum of channels, spanning both traditional (e.g. TV, print, billboards etc.) and digital media. The Brand ads team is responsible for designing optimal solutions that would help these advertisers achieve their marketing objectives on YouTube. Given the relative nascency of programmatic digital platforms such as YouTube in the marketing portfolio of brand advertisers, our team of multi-skilled software engineers, data scientists, and product managers manage foundational problems that present a mix of technical complexity and business headroom.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities
  • Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
  • Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
  • Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
  • Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Independently format, re-structure, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
  • Build optimization solutions encompassing targeting relevance, creative recommendation, bidding optimization, and impact measurement.

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