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Data Analytics Engineer Jobs in Berkeley, CA (NOW HIRING)

Fieldguide

Senior Analytics Engineer

San Francisco, CA · On-site

$175K - $200K/yr

About the Role The Data Science team at Fieldguide builds Fieldguide Insights , a product that ... We are hiring a Senior Analytics Engineer to own the foundation that Insights runs on: the semantic ...

Fieldguide

Senior Analytics Engineer

San Francisco, CA · Remote

$175K - $200K/yr

About the Role The Data Science team at Fieldguide builds Fieldguide Insights , a product that ... We are hiring a Senior Analytics Engineer to own the foundation that Insights runs on: the semantic ...

Poshmark, Inc

Director, Data Analytics

Redwood City, CA · On-site

Partner with Data Engineering to productionize frameworks. Hire, mentor, and grow a team of analysts and promote a data-driven culture across the Revenue org. What Success Looks Like In Your First 6 ...

Redwood Materials

Senior Analytics Engineer

San Francisco, CA · On-site

$123K - $169K/yr

About the Role Our central data and analytics team builds and operates the data pipelines that power reporting, analytics, and automation across Redwood. This is a hands-on engineering role: the ...

Redwood Materials

Senior Analytics Engineer

San Francisco, CA · On-site

$123K - $169K/yr

About the Role Our central data and analytics team builds and operates the data pipelines that power reporting, analytics, and automation across Redwood. This is a hands-on engineering role: the ...

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All JobsData Analytics Engineer JobsData Analytics Engineer Jobs in Berkeley, CA

Data Analytics Engineer information

See Berkeley, CA salary details

$54.5K

$158.8K

$217.3K

How much do data analytics engineer jobs pay per year?

As of Aug 20, 2026, the average yearly pay for data analytics engineer in Berkeley, CA is $158,830.00, according to ZipRecruiter salary data. Most workers in this role earn between $140,200.00 and $168,400.00 per year, depending on experience, location, and employer.

How do data analytics engineers typically collaborate with data scientists and business stakeholders on projects?

Data Analytics Engineers play a crucial role in bridging the gap between raw data and actionable insights by building, optimizing, and maintaining data pipelines. They often work closely with data scientists to ensure data is clean, accessible, and structured for advanced analytics or machine learning models. Additionally, they collaborate with business stakeholders to understand reporting requirements and ensure that data solutions align with organizational objectives. Regular communication and cross-functional teamwork are essential aspects of this role, as engineers must translate business needs into technical specifications and deliver reliable data products.

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

To thrive as a Data Analytics Engineer, you need strong proficiency in data modeling, SQL, and statistical analysis, typically supported by a degree in computer science, statistics, or a related field. Familiarity with tools such as Python, R, Apache Spark, Tableau, and cloud data platforms like AWS or Google BigQuery is essential, along with relevant certifications. Excellent problem-solving, communication, and collaboration skills help you translate data insights into actionable business solutions. These skills and qualities are crucial for designing robust data pipelines and enabling data-driven decision-making across organizations.

What is the difference between Data Analytics Engineer vs Data Scientist?

AspectData Analytics EngineerData Scientist
CredentialsBachelor's or master's in CS, Data Science, or related fields; certifications like Google Data AnalyticsBachelor's or master's in CS, Statistics, or related fields; certifications like Certified Data Scientist
Work EnvironmentFocus on building data pipelines, dashboards, and analytics toolsFocus on statistical modeling, machine learning, and data exploration
Employer & Industry UsageUsed across tech, finance, healthcare for data infrastructure and analyticsCommon in research, product development, and advanced analytics teams

While both roles work with data, Data Analytics Engineers primarily develop data infrastructure and tools for analysis, whereas Data Scientists focus on statistical modeling and machine learning to generate insights. They often collaborate but have distinct technical focuses.

What does a data analytics engineer do?

A data analytics engineer designs, builds, and maintains data pipelines and infrastructure to collect, process, and analyze large datasets. They use tools like SQL, Python, and cloud platforms to enable data-driven decision-making and often collaborate with data scientists and business teams to develop insights and reports.

What are popular job titles related to Data Analytics Engineer jobs in Berkeley, CA?

For Data Analytics Engineer jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Data Analytics Engineer jobs in Berkeley, CA look for?

The top searched job categories for Data Analytics Engineer jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Data Analytics Engineer jobs?

Cities near Berkeley, CA with the most Data Analytics Engineer job openings:

Data Analytics Engineer jobs near you
Infographic showing various Data Analytics Engineer job openings in Berkeley, CA as of August 2026, with employment types broken down into 79% Full Time, and 21% Contract. Highlights an 100% In-person job distribution, with an average salary of $158,830 per year, or $76.4 per hour.

Senior Analytics Engineer

Fieldguide

San Francisco, CA • On-site

$175K - $200K/yr

Full-time

Life, Retirement, PTO

Re-posted 17 days ago

Job description

About Us
Fieldguide is establishing a new state of trust for global commerce and capital markets by automating and streamlining the work of assurance and audit practitioners, specifically in cybersecurity, privacy, and financial audits. Put simply, we build software for the people who enable trust between businesses.
We're based in San Francisco, CA and we're backed by top investors, including Growth Equity at Goldman Sachs Alternatives, Bessemer Venture Partners, 8VC, Floodgate, Y Combinator, DNX Ventures, Global Founders Capital, Justin Kan, Elad Gil, and more.
We value diversity-in backgrounds and experiences. We need people from all backgrounds and walks of life to help build the future of audit and advisory. Fieldguide's team is inclusive, driven, humble, and supportive. We are deliberate and self-reflective about the kind of team and culture we are building, seeking teammates who are not only strong in their own aptitudes, but who also care deeply about supporting each other's growth.
As an early-stage startup employee, you'll have the opportunity to help build the future of business trust. We make audit practitioners' lives easier by consolidating up to 50% of their work and improving work-life balance. If you share our values and enthusiasm for building a great culture and product, you'll find a home at Fieldguide.
About the Role
The Data Science team at Fieldguide builds Fieldguide Insights, a product that delivers new-to-the-industry visibility into audit and advisory execution and performance. We are hiring a Senior Analytics Engineer to own the foundation that Insights runs on: the semantic layer, the pipelines, and the models that make every number we ship reliable and defensible.
This is a role for a technical, broad engineer who specializes in analytics. You will define the canonical tables, columns, and metrics behind our customer-facing reports, move production logic into governed dbt, and harden pipelines. Your work is the base that our API and MCP-based analytics expose, so correctness is non-negotiable.
You will partner closely with Product, Engineering, App Platform, and Infrastructure. This role is ideal for a polymath who wants to model the business in tables that hold up over time and shape how audit and advisory firms consume analytics.
What You'll Do
  • Design and own the Insights semantic layer, establishing canonical definitions for the tables, columns, and metrics behind each report so the numbers we ship are trusted and consistent across the business.
  • Standardize how we define core metrics, reconcile them across data sources such as Amplitude, and clearly document any variances.
  • Migrate production report logic into governed, tested, and portable dbt models, strengthening test coverage and reusable macros along the way.
  • Improve the reliability of business-critical pipelines through proactive monitoring, alerting, and resilience to upstream schema changes.
  • Partner with customers and internal teams on data quality and new environment launches, including triage, onboarding, and data handling requirements.
  • Contribute to the design of a Kimball-style dimensional warehouse built to serve both traditional BI and emerging LLM and MCP-based analytics.
  • Help drive the migration of BI reporting onto the semantic layer, from report inventory and classification through cutover planning.
Who You Are
  • 4+ years in a modern data stack, with advanced SQL and deep hands-on dbt expertise across modeling, testing, and project structure.
  • An engineer at heart: you ship production-quality Python, work fluently with version control and CI, and are comfortable diagnosing pipeline failures end to end.
  • A skilled dimensional modeler (Kimball, slowly changing dimensions) who designs data models built to last as the business evolves.
  • Analytically fluent, with a strong sense for data quality and the judgment to validate that results are correct, not just computed.
  • Deep experience with BigQuery and cloud data warehousing, including cost and performance optimization.
  • A collaborative partner who thrives working across teams, coordinating with Infrastructure and App Platform teams to deliver shared outcomes.
  • Based in SF and energized by in-person collaboration with our team.
Bonus Points
  • A generalist background, with prior time as a data engineer, software engineer, or data scientist on top of the analytics-engineering core.
  • Experience feeding LLM or AI-native data interfaces (semantic layer, MCP, text-to-SQL guardrails).
  • BI migration experience (Looker, Omni, Tableau).
  • A regulated-domain background where correctness is non-negotiable.
More About Fieldguide
Fieldguide is a values-based company. Our values are:
  • Fearless - Inspire and break down seemingly impossible walls
  • Fast - Launch fast with excellence; iterate to perfection
  • Lovable - Deliver happiness and 11-star experiences
  • Owners - Execute and run the business with ownership
  • Win-win - Create mutual value and earn trust for life
  • Inclusive - Scale the best ideas with inclusive teams
Some of our benefits include:
  • Competitive compensation packages with meaningful ownership
  • Flexible PTO
  • 401(k)
  • Wellness benefits starting on your first day
  • Technology and work-from-home reimbursement
  • Flexible work schedules

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