ZipRecruiter

Log In

1

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

Parafin, Inc

Data Analytics Engineer

San Francisco, CA · On-site

$180K - $220K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

About the Position We are looking for a Data Analytics Engineer to build and scale the data models, pipelines, and analytics infrastructure that power decision-making across Parafin. You'll design ...

Parafin, Inc

Data Analytics Engineer

San Francisco, CA · On-site

$180K - $220K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

About the Position We are looking for a Data Analytics Engineer to build and scale the data models, pipelines, and analytics infrastructure that power decision-making across Parafin. You'll design ...

Parafin

Data Analytics Engineer

San Francisco, CA · Remote

$180K - $220K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

About the Position We are looking for a Data Analytics Engineer to build and scale the data models, pipelines, and analytics infrastructure that power decision-making across Parafin. You'll design ...

Peterson

Data & Analytics Engineer

San Leandro, CA · On-site

$129K - $155K/yr

Peterson Cat has a need for a Data & Analytics Engineer to work onsite at our San Leandro, CA location. WE ARE UNABLE TO PROVIDE SPONSORSHIP AT THIS TIME SUMMARY The Data & Analytics Engineer is ...

PetersonCat

Data & Analytics Engineer

San Leandro, CA · On-site

$129K - $155K/yr

Peterson Cat has a need for a Data & Analytics Engineer to work onsite at our San Leandro, CA location. WE ARE UNABLE TO PROVIDE SPONSORSHIP AT THIS TIME SUMMARY The Data & Analytics Engineer is ...

Venturesoft

Data Analytics Engineer

Pleasanton, CA · On-site

$126K - $151K/yr

  • PTO

In this role as Data Analytics Engineer, In this role, you will build and maintain data pipelines, tools, and visualizations to enable organizational insights. You'll develop KPI reports, partner ...

Broccoli AI

Founding Data Analytics Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Ad-hoc analyses, segment investigations, partner questions. * Work closely with engineering. Understand how our systems store and produce data, including schemas, events, and architecture, and give ...

New

Hyperbolic

Senior Data Analytics Engineer

San Francisco, CA · On-site

$124K - $169K/yr

They are seeking their first data analytics hire to establish and own the data foundation for their ... Hyperbolic is the open-access AI cloud made for AI developers, providing fast, affordable access to ...

DoorDash

Analytics Engineer

San Francisco, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

About the Team The Analytics Engineering team at DoorDash is embedded within the Analytics and Data ... Data is fundamental to DoorDash's success, and this team plays a critical role in enabling high ...

next page

Showing results 1-20

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 19, 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.

Data Analytics Engineer

ExlService Holdings, Inc.

San Francisco, CA • On-site

$140K - $155K/yr

Full-time

Posted 14 days ago

ExlService Holdings rating

7.8

Company rating: 7.8 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

134th of 492 rated business services

Job description


We are seeking an experienced Data Analytics Engineer to design, build, and optimize scalable data pipelines and analytics infrastructure that power critical financial products and decisions. You will work at the intersection of software engineering and data analytics - building reliable ETL/ELT pipelines, orchestrating cloud-native workflows, and enabling trusted, high-quality data for reporting, risk, and product teams. This role requires strong engineering discipline (version control, CI/CD, infrastructure-as-code) combined with deep SQL and PySpark expertise, ideally within a regulated banking or financial services environment.
Responsibilities
  • Design, build, and maintain scalable, reliable ETL/ELT data pipelines across cloud and on-prem sources, ensuring data quality, lineage, and auditability.
  • Develop and optimize Python/ PySpark and SQL-based data transformations for large-scale, high-volume financial datasets.
  • Architect and manage data pipeline orchestration (e.g., Airflow, Databricks Workflows, Step Functions) to automate ingestion, transformation, and delivery.
  • Build and maintain CI/CD pipelines using GitHub/GitHub Actions to support automated testing, deployment, and version-controlled infrastructure changes.
  • Develop cloud-based solutions on AWS (S3, Glue, EMR, Redshift, Lambda, IAM) supporting analytics, reporting, and downstream ML use cases.
  • Deploy and manage infrastructure and pipelines as code, following best practices for environment promotion, rollback, and monitoring.
  • Monitor, troubleshoot, and optimize pipeline performance, query efficiency, and cost across the data stack.
  • Partner with data scientists, analysts, product, and risk/compliance teams to translate business requirements into robust data solutions.
  • Enforce data governance, security, and regulatory compliance standards appropriate for financial data (PII, SOX, PCI, etc.).
  • Document pipeline architecture, data models, and processes; contribute to engineering standards and code review practices.

Qualifications
  • Required Technical Skills
  • Advanced proficiency in Python for scripting, automation, and data engineering workflows.
  • Strong hands-on experience with PySpark for distributed data processing at scale.
  • Expert-level SQL and Advanced SQL (window functions, query optimization, complex joins, performance tuning).
  • Solid experience with AWS cloud services and cloud-based application/data development (S3, Glue, EMR, Redshift, Lambda, IAM, CloudWatch).
  • Proven expertise building and orchestrating data pipelines (Airflow, Databricks Workflows, Step Functions, or equivalent).
  • Hands-on CI/CD experience using GitHub / GitHub Actions for automated build, test, and deployment.
  • Deep understanding of ETL/ELT design patterns, data modeling, and data warehousing concepts.
  • Experience deploying infrastructure and pipelines via code (e.g. version-controlled deployments).
  • Demonstrated ability to optimize pipeline performance, query execution, and cloud resource/cost efficiency.
    Preferred / Desired Skills (Nice to Have)
  • Hands-on experience with Databricks (Delta Lake, Unity Catalog, notebooks, cluster optimization).
  • Familiarity with Terraform or CloudFormation for infrastructure as code.
  • Experience with streaming data technologies (Kafka, Kinesis, Spark Structured Streaming).
  • Exposure to data quality/testing frameworks (Great Expectations, Dbt tests).
  • Knowledge of Dbt for transformation and analytics engineering workflows.
  • Understanding of financial data domains - payments, lending, risk, fraud, or accounting data.
  • Relevant certifications (AWS Certified Data Analytics/Solutions Architect, Databricks Certified Data Engineer).
    Qualifications
  • Bachelor's degree in computer science, Engineering, Data Science, or a related field (or equivalent practical experience).
  • 5+ years of experience in data engineering, analytics engineering, or a related technical role.
  • Prior experience working within banking, fintech, or financial services, with awareness of regulatory and data-security requirements.
  • Demonstrated track record delivering production-grade data pipelines in a cloud environment.
  • Soft Skills
  • Strong analytical and problem-solving skills with attention to detail and data accuracy.
  • Excellent communication skills; able to translate technical concepts for non-technical stakeholders.
  • Collaborative mindset with experience working cross-functionally with analysts, engineers, and business teams.
  • Self-directed and comfortable owning projects end-to-end in a fast-paced, regulated environment.
    Strong ownership mentality around data quality, reliability, and documentation.
    Base Compensation Range: $140,000- $155,000
    The posted range is the hiring range for this role - a subset of the broader range available to employees over time - and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

About Us
EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world's leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.
EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL's Human Resources team, as well as our hiring managers.
About the Team
EXL is the indispensable partner for leading businesses in data-led industries such as insurance, banking and financial services, healthcare, retail and logistics. We bring a unique combination of data, advanced analytics, digital technology and industry expertise to help our clients turn data into insights, streamline operations, improve customer experience, and transform their business. Our partnerships with clients are built on a foundation of collaboration - and we've been chosen as a partner by nine of the top ten leading US insurance companies, nine of the top 20 global banks, and six of the top ten US health care payers. We function as one team to make your goals our goals, whether that's unlocking the value of generative AI or embedding analytics into workflows that reduce risk or power your growth. Clients choose EXL as their transformation partner for many reasons. Our geographic diversity make talent all over the world instantly accessible. Digital accelerators enable unmatched speed-to-value, letting you realize results fast. It's our people that truly set us apart, though, including the 1,500 data scientists we have dedicated to our generative AI practice. And our more than twenty years of experience in delivering business services, garnering stellar client references, and maintaining a solid balance sheet are reassuring to our C-suite clients. Find out for yourself why clients, employees, and analysts think we're some of the best in the business. Contact us to see how we can help you achieve your goals.

What ExlService Holdings employees say

Pay

Hours and flexibility

Workplace

Get the full story on Breakroom

Apply