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Manager Data Analytics Engineer Jobs in California

PetersonCat

Data & Analytics Engineer

San Leandro, CA · On-site

$129K - $155K/yr

Management may assign or reassign the functions to this job at any time due to reasonable ... data engineering, business intelligence, or analytics roles, preferably in a heavy industrial ...

Parafin, Inc

Data Analytics Engineer

San Francisco, CA · On-site

$180K - $220K/yr

... management, and savings tools to their small business users via a simple integration. Parafin takes ... About the Position We are looking for a Data Analytics Engineer to build and scale the data models ...

Heluna Health

Data/Analytics Engineer

Los Angeles, CA · On-site

$10K/mo

Design and manage automated data lifecycles using GitHub Actions and Terraform (Infrastructure as ... Provide oversight and mentorship to data engineers, analysts, and scientists, setting engineering ...

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 ... Translate ambiguous requests from Finance, Marketing, Revenue Management, Operations, and ...

Venturesoft

Data Analytics Engineer

Pleasanton, CA · On-site

$126K - $151K/yr

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 ...

Mattel

Manager, Data Analytics

El Segundo, CA · On-site

Partner closely with data engineering teams to design and improve scalable data pipelines, data ... analytics, CRM, marketing systems, and operational data * Contribute to improvements in data ...

Mattel

Manager, Data Analytics

El Segundo, CA · On-site

Partner closely with data engineering teams to design and improve scalable data pipelines, data ... analytics, CRM, marketing systems, and operational data * Contribute to improvements in data ...

Headspace

Manager, Data Analytics

Santa Monica, CA · On-site +1

$158K - $224K/yr

About the Manager, Data Analytics at Headspace: What you will do: * Drive significant business ... Provide thought leadership by collaborating with Product, Engineering, Marketing, and Operations to ...

Mattel

Manager, Data Analytics

El Segundo, CA · On-site

Partner closely with data engineering teams to design and improve scalable data pipelines, data ... analytics, CRM, marketing systems, and operational data * Contribute to improvements in data ...

Headspace

Manager, Data Analytics

Santa Monica, CA · On-site

$158K - $224K/yr

About the Manager, Data Analytics at Headspace: What you will do: * Drive significant business ... Provide thought leadership by collaborating with Product, Engineering, Marketing, and Operations to ...

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Manager Data Analytics Engineer information

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

AspectManager Data Analytics EngineerData Analytics Engineer
Required CredentialsBachelor's or Master's in Data Science, Analytics, or related field; often leadership experienceBachelor's or Master's in Data Science, Analytics, or related field
Work EnvironmentLeads teams, manages projects, collaborates with stakeholdersDevelops data models, analyzes data, implements solutions
Employer & Industry UsageUsed in tech, finance, healthcare, and large enterprisesCommon in similar industries, often within data teams

The main difference is that a Manager Data Analytics Engineer oversees teams and projects, focusing on leadership and strategic planning, while a Data Analytics Engineer primarily develops and implements data solutions. Both roles require strong technical skills, but the manager role adds a layer of team management and stakeholder communication.

How does a manager data analytics engineer typically balance technical project work with team leadership responsibilities?

As a Manager Data Analytics Engineer, you are expected to split your time between overseeing complex analytics engineering tasks and guiding your team’s development. This involves setting project priorities, conducting code reviews, and ensuring data solutions align with business goals, while also mentoring team members and facilitating collaboration with stakeholders like data scientists and business analysts. Successful managers often establish clear communication channels and delegate tasks effectively, so they can stay hands-on with key projects while supporting the professional growth of their team.

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

To thrive as a Manager Data Analytics Engineer, you need a strong background in data engineering, analytics, and leadership, typically with a degree in computer science or a related field. Familiarity with tools like SQL, Python, data warehousing platforms (e.g., Snowflake, Redshift), and certifications in cloud technologies or data management are common requirements. Excellent communication, problem-solving, and team management skills set top performers apart in this role. These competencies are essential for driving data strategy, ensuring data quality, and leading analytics teams to deliver actionable business insights.

What is a manager data analytics engineer?

A Manager Data Analytics Engineer is a professional who leads a team of data analytics engineers responsible for designing, building, and maintaining data systems and analytics solutions. They oversee data pipeline development, ensure data quality, and collaborate with stakeholders to translate business requirements into technical solutions. In addition to technical expertise, they manage project timelines, mentor team members, and help drive data-driven decision-making across the organization.
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 Manager Data Analytics Engineer jobs in California? For Manager Data Analytics Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Manager Data Analytics Engineer jobs in California look for? The top searched job categories for Manager Data Analytics Engineer jobs in California are:
What cities in California are hiring for Manager Data Analytics Engineer jobs? Cities in California with the most Manager Data Analytics Engineer job openings:
Manager Data Analytics Engineer jobs near you

Data Analytics Engineer

ExlService Holdings, Inc.

San Francisco, CA • On-site

$140K - $155K/yr

Full-time

Posted 6 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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