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Junior Data Analytics Engineer Jobs in California

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 ...

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 ...

Heluna Health

Data/Analytics Engineer

Los Angeles, CA · On-site

$10K/mo

This role sits at the intersection of data engineering and advanced analytics, responsible for the end-to-end design, implementation, and management of a governed Medallion Architecture (Bronze ...

Heluna Health

Data/Analytics Engineer

Los Angeles, CA · On-site

This role sits at the intersection of data engineering and advanced analytics, responsible for the end-to-end design, implementation, and management of a governed Medallion Architecture (Bronze ...

AmaWaterways, LLC

Data Analytics Engineer

Calabasas, CA · On-site

$90K - $100K/yr

Role Summary AmaWaterways is hiring a Data Analytics Engineer to own the analytics layer of our modern data platform. You will design governed data marts, build the semantic layer that powers our ...

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

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 ...

Datafied

Junior Data Analyst

Anaheim, CA · On-site +1

We are seeking a detail-oriented and analytical Junior Data Analyst to join our team. This role is responsible for transforming raw data into actionable insights that support strategic decision ...

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 ...

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Junior Data Analytics Engineer information

What is a junior data analytics engineer?

A Junior Data Analytics Engineer is an entry-level professional who assists in collecting, processing, and analyzing data to help organizations make informed decisions. They typically work with data pipelines, databases, and analytical tools to support senior data engineers and analysts. Their responsibilities often include cleaning data, writing basic queries, and creating simple reports or dashboards. This role serves as a foundation for more advanced positions in data engineering and analytics.

What are the key skills and qualifications needed to thrive as a junior data analytics engineer?

To thrive as a Junior Data Analytics Engineer, you need a solid understanding of data analysis, statistics, and programming languages such as Python or SQL, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with data visualization tools (e.g., Tableau, Power BI), database management systems, and cloud platforms is commonly expected. Strong problem-solving skills, attention to detail, and effective communication make candidates stand out in this role. These abilities are crucial for accurately analyzing data, translating findings into actionable insights, and enabling data-driven decision-making within organizations.

What are the most common challenges faced by a junior data analytics engineer when transitioning from academic projects to real-world business data?

One of the most common challenges for Junior Data Analytics Engineers is adapting to the complexities of real-world data, which is often incomplete, inconsistent, or unstructured compared to clean academic datasets. Additionally, there is a stronger emphasis on collaboration with cross-functional teams and communicating findings to non-technical stakeholders. Learning to balance technical analysis with business objectives, and managing multiple tasks or project deadlines, are also typical hurdles. Overcoming these challenges helps junior engineers grow quickly and become valuable contributors to their teams.

What is the difference between Junior Data Analytics Engineer vs Data Analyst?

AspectJunior Data Analytics EngineerData Analyst
Required SkillsBasic programming, data modeling, SQL, data pipeline understandingData visualization, statistical analysis, Excel, SQL
Work EnvironmentCollaborates with data engineers and developers, often in tech or finance sectorsWorks with business teams to interpret data, in various industries
CertificationsSQL, Python, entry-level data certificationsExcel, Tableau, Power BI certifications

Junior Data Analytics Engineers focus on building data pipelines and integrating data systems, requiring programming skills. Data Analysts primarily interpret data through visualization and statistical methods. Both roles often overlap but serve different core functions within data teams.

What are the most commonly searched types of Data Analytics Engineer jobs in California? The most popular types of Data Analytics Engineer jobs in California are:
What are popular job titles related to Junior Data Analytics Engineer jobs in California? For Junior Data Analytics Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Junior Data Analytics Engineer jobs in California look for? The top searched job categories for Junior Data Analytics Engineer jobs in California are:
What cities in California are hiring for Junior Data Analytics Engineer jobs? Cities in California with the most Junior Data Analytics Engineer job openings:
Junior Data Analytics Engineer jobs near you
Infographic showing various Junior Data Analytics Engineer job openings in California as of August 2026, with employment types broken down into 77% Full Time, 6% Temporary, and 17% Contract. Highlights an 94% In-person, and 6% Remote job distribution.

Data Analytics Engineer

ExlService Holdings, Inc.

San Francisco, CA • On-site

$140K - $155K/yr

Full-time

Posted 7 days ago

ExlService Holdings rating

7.8

Company rating: 7.8 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

132nd of 488 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.

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