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

Pave

Analytics Engineer

San Francisco, CA · On-site

$202K - $222K/yr

The Data Team @ Pave As part of the Data team at Pave you will help us redefine how companies ... Experience - 4+ years of experience in a Data/Analytics Engineering role, ideally in a product ...

Zipline

Senior Analytics Engineer

South San Francisco, CA

$125K - $172K/yr

We design and build Zipline's central data platform to provide mission-critical insights for ... As a Senior Analytics Engineer, you are the owner of analytics infrastructure and datasets that ...

Plaid

Senior Analytics Engineer

San Francisco, CA · On-site

$123K - $169K/yr

The Analytics Engineering team owns the full-stack analytics foundation for Plaid's GTM, CGX, NEA ... We build and maintain the core semantic layer data models (dbt on Databricks), activation layer ...

Zipline

Senior Analytics Engineer

San Francisco, CA

$123K - $169K/yr

We design and build Zipline's central data platform to provide mission-critical insights for ... As a Senior Analytics Engineer, you are the owner of analytics infrastructure and datasets that ...

Gridmatic Inc

Analytics Engineer

San Francisco, CA · Hybrid

$175K - $208K/yr

As a power company driven by ML and data, your scope would include automating gross margin, domain ... Have had 3+ years of experience in analytics engineering or similar roles. * Have strong technical ...

Pave

Analytics Engineer

San Francisco, CA · On-site

$190/hr

The Data Team @ Pave As part of the Data team at Pave you will help us redefine how companies ... Experience - 4+ years of experience in a Data/Analytics Engineering role, ideally in a product ...

SpaceXAI

Analytics Engineer - X

San Francisco, CA

$180K - $440K/yr

We are seeking a skilled Analytics Engineer to build and maintain robust data systems that enable high-impact quantitative analysis and business decision-making. This role combines strong software ...

1 point system

Marketing Data Analytics

San Francisco, CA · Remote

$88K - $110K/yr

MUST HAVES: 8+ yrs of IT/Engineering 4-yr Degree (Computer Science or Related) 5+ yrs of Data Analytics, with specific work with Marketing contribution to Partner-Sourced Pipelines 3+ yrs Data ...

EV Realty

Intern, Data Analytics

San Francisco, CA · On-site +1

The Role As a Data Analytics Intern you will help build and maintain various analytics tools and ... Programming Proficiency and experience in Python, SQL, or similar languages. Python and SQL ...

EV Realty

Intern, Data Analytics

San Francisco, CA · On-site

The Role As a Data Analytics Intern you will help build and maintain various analytics tools and ... Programming Proficiency and experience in Python, SQL, or similar languages. Python and SQL ...

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Showing results 21-40

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.

Member of Data Staff (Analytics Engineer)

Perplexity AI

San Francisco, CA • On-site

$134K - $162K/yr

Other

Posted 6 days ago

Job description

Perplexity is AI for people who expect more. On the data team, that means building the systems that make our data reliable, understandable, and usable by both humans and AI.
We're looking for an analytics engineer or data engineer who wants to build the foundation for an AI-native data organization. You'll design core data models, pipelines, semantic layers, data quality systems, governance practices, and warehouse workflows that power the entire company: helping teams make strategic decisions, operate the business, and move faster with trusted data. You'll also make sure those systems are secure, privacy-aware, and legible to AI agents, data scientists, and the rest of the company.
This role is for someone who can operate at the boundary of analytics engineering, data engineering, data governance, and internal product. You care about dimensional modeling, dbt standards, cost-aware warehouse design, access controls, privacy, and the details that make data trustworthy. You also believe AI should make the data stack faster, easier to maintain, and more accessible across the company without weakening security or governance.
What You'll Do
  • Build the core data foundation - design and maintain high-quality data models, marts, and pipelines that make analysis fast, reliable, and reusable.
  • Manage the data warehouse - help own warehouse architecture, environments, permissions, performance, cost, data lifecycle, and operational hygiene so the platform scales cleanly.
  • Make the warehouse AI-readable - own the documentation, semantic context, metadata, lineage, and retrieval patterns that AI systems depend on to understand and query Perplexity's data correctly.
  • Own data modeling standards - define and champion dbt patterns, dimensional modeling practices, naming conventions, tests, and review processes.
  • Lead data governance practices - define standards for access, ownership, lineage, documentation, retention, quality, and sensitive data handling across the analytical warehouse.
  • Build with security and privacy in mind - partner with engineering, security, legal, and finance where needed to ensure data access, sharing, and AI-enabled workflows are appropriate and controlled.
  • Automate data quality and maintenance - build AI-assisted workflows that detect issues, explain root causes, suggest fixes, generate tests, and reduce manual firefighting.
  • Improve data team productivity - automate repetitive workflows, improve tooling, streamline development, and make it easier for data scientists and stakeholders to answer questions.
  • Partner across the company - work closely with data scientists, engineering, product, finance, and GTM teams to translate analytical needs into durable data systems.
  • Shape tooling decisions - evaluate build-versus-buy tradeoffs, manage vendor relationships when needed, and choose tools that scale with the team.
What We're Looking For
  • 6+ years of experience as an analytics engineer, data engineer, data scientist, or closely related role.
  • Deep SQL expertise - you can reason about correctness, performance, joins, grain, and edge cases in complex warehouse queries.
  • Strong data modeling experience - you've worked hands-on with dbt (or a similar transformation framework) in production, and you understand dimensional modeling, data contracts, testing, and how analytical schemas should evolve.
  • Pipeline ownership - you've built, maintained, debugged, and improved production data pipelines.
  • Warehouse management experience - you've worked with warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership.
  • Governance mindset - you think clearly about data ownership, access controls, privacy, retention, lineage, auditability, and the risks of making data too easy to access.
  • AI-native working style - you already use AI to speed up development, documentation, QA, exploration, and repetitive workflow automation.
  • Stakeholder fluency - you know how to turn messy analytical requirements into trusted models, metrics, and reusable data assets.
  • Autonomy and execution - you can take projects from ambiguous problem to production-quality system with minimal oversight.
  • Operational judgment - you care about reliability, governance, security, cost, and long-term maintainability.
Bonus
  • Snowflake administration, optimization, cost management, or warehouse performance tuning.
  • Experience with RBAC, PII handling, data classification, retention policies, audit workflows, or privacy/security reviews.
  • Experience with Databricks or other modern data infrastructure.
  • Experience building semantic layers, metrics layers, metadata systems, or data catalogs.
  • Python experience for data tooling, automation, orchestration, or quality checks.
  • Previous experience as an early analytics engineer or data engineer at a high-growth startup.
Why This Role
  • Own the foundation - your work will determine how fast and confidently the company can use data.
  • Build for humans and AI - the next generation of data infrastructure needs to be understandable by agents as well as analysts.
  • High leverage - better models, pipelines, and tools multiply every data scientist and every stakeholder who depends on data.
  • Small team, broad scope - you'll have room to define standards, choose tools, and ship systems that become company-wide defaults.

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