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Google Big Data Jobs (NOW HIRING)

Stellantis

Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

Comprehensive experience working with Big Data platforms (i.e., Spark, Google Big Query, Azure, AWS S3, etc.) * Familiarity with time series database, data streaming applications, event driven ...

IntraEdge

GCP Data Engineer

Austin, TX

$113K - $136K/yr

You need to have expertise in Google Big Query, Google Cloud Storage, Dataflow, Cloud Composer, Python, and SQL will be crucial in developing effective data solution that support our fraud data ...

Stellantis

Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

... Google Big Query, Azure, AWS S3, etc.) • Familiarity with time series database, data streaming applications, event driven architectures, Kafka, Flink, and more • Experience with workflow ...

Navtech, Inc.

GCP Data Architect

Columbus, OH · On-site

$61.50 - $79.25/hr

Google Big Data Specialty Certification • 15+ years direct experience working in Enterprise Data Warehouse / Data Lake technologies • 10+ years in a customer facing role working with enterprise ...

ZipRecruiter

Senior Software Engineer, Big Data

Seattle, WA · Remote

$140K - $200K/yr

Experience with Hadoop, Spark, Hive, and/or other big data technologies * Comprehensive computer ... Familiarity with Apache Hudi, Apache Beam, Apache Flink, Google Cloud Dataflow, Amazon Kinesis Data ...

Futran Tech Solutions Pvt. Ltd.

GCP DevOps

Baltimore, MD · On-site

$52.50 - $71.75/hr

... experience using Google's big data technology stack, including big query, big query storage, big query analytics and cloud data prep • Display behaviors in line with our Critical People ...

ZipRecruiter

Senior Software Engineer, Big Data

Seattle, WA · Remote

$140K - $200K/yr

Experience with Hadoop, Spark, Hive, and/or other big data technologies * Comprehensive computer ... Familiarity with Apache Hudi, Apache Beam, Apache Flink, Google Cloud Dataflow, Amazon Kinesis Data ...

ZipRecruiter

Senior Software Engineer, Big Data

Seattle, WA · Remote

$140K - $200K/yr

Experience with Hadoop, Spark, Hive, and/or other big data technologies * Comprehensive computer ... Familiarity with Apache Hudi, Apache Beam, Apache Flink, Google Cloud Dataflow, Amazon Kinesis Data ...

Innosoul inc

Data Warehouse Architect

Richmond, VA · On-site

Data Architecture Required 5 Years Data Processing Required 5 Years Oracle Health (formerly Cerner) Required 3 Years Google Big Query Highly desired 2 Years Python Highly desired 5 Years Google Cloud ...

New

Megan soft Inc

GCP Devops Engineer (W2 Position)

Dearborn, MI · On-site

$48.50 - $66.50/hr

Experience working in GCP based Big Data deployments (Batch/Real-Time) leveraging Terraform, BigQuery, Bigtable, Google Cloud Storage, PubSub, Kafka, Data Fusion, Dataflow, Dataproc, Cloud Build ...

Intraedge

Google Cloud Platform Data Engineer

Phoenix, AZ · On-site

$113K - $136K/yr

Google Cloud Platform Data Engineer (Day 1 onsite - Hybrid 3 days a week in office) Location ... Hands-on expertise with application design and software development in Big Data (Spark(Pyspark ...

KK Associates

Big Data Technical Architect

Manhattan, NY · On-site

$70.25 - $90.50/hr

Exceptional hands-on proficiency and deep architectural understanding of the Big Data ecosystem ... or Google Cloud Platform (e.g., Dataproc, BigQuery, Cloud Storage). * Expert-level hands-on ...

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All JobsGoogle Big Data Jobs

Google Big Data information

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$46K

$165K

$243.5K

How much do google big data jobs pay per year?

As of Jul 17, 2026, the average yearly pay for google big data in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

Is big data a good career option?

A career in big data, including roles like data engineer or data analyst, is considered promising due to the increasing demand for data-driven decision making across industries. Success often requires skills in programming, data management tools, and analytics platforms like Hadoop or Spark. The field offers competitive salaries and growth opportunities for those with technical expertise and certifications.

What types of projects or challenges can I expect to work on in a Google Big Data role?

In a Google Big Data position, you can expect to work on large-scale data integration, processing, and analysis projects that support business, product development, or customer insights. Typical challenges include designing scalable data pipelines, optimizing queries on massive datasets, ensuring data quality, and implementing advanced analytics or machine learning solutions. You'll often collaborate with data scientists, engineers, and product teams, using a variety of Google Cloud and open-source tools. This work environment is dynamic and encourages innovation, problem-solving, and continuous learning, providing opportunities to shape both technical solutions and strategic business outcomes.

What is the salary of big data analyst in Google?

The salary of a Big Data Analyst at Google typically ranges from $80,000 to $150,000 annually, depending on experience, location, and skill level. Google values expertise in data analysis tools like SQL, Python, and BigQuery, and often offers competitive compensation packages for this role.

What are the key skills and qualifications needed to thrive in the Google Big Data position, and why are they important?

To thrive in a Google Big Data role, you need a strong background in data engineering, distributed systems, and analytical problem-solving, typically supported by a degree in computer science or a related field. Mastery of big data technologies such as Hadoop, Spark, Cloud Dataflow, and familiarity with programming languages like Python or Java are essential, along with certifications in Google Cloud Platform (GCP) being highly desirable. Effective collaboration, strong communication skills, and the ability to translate complex data insights into actionable business recommendations help you excel on cross-functional teams. These skills and qualities are crucial for designing scalable big data solutions, uncovering meaningful insights, and driving impactful business decisions within a fast-paced tech environment.

What is a Google Big Data job?

A Google Big Data job typically involves working with large-scale data storage, processing, and analytics using Google Cloud technologies like BigQuery, Dataflow, and Dataproc. Professionals in this field design, optimize, and manage data pipelines to help businesses extract insights from massive datasets. Responsibilities often include data engineering, machine learning integration, and performance tuning to ensure efficient data operations. Strong knowledge of cloud computing, SQL, and distributed computing frameworks is usually required.

What tech jobs pay $400,000 a year?

In the tech industry, senior roles such as Data Engineering Managers, Cloud Architects, and Machine Learning Directors can reach or exceed $400,000 annually, especially with extensive experience, advanced skills in cloud platforms like AWS or GCP, and leadership responsibilities. These positions often require advanced degrees, certifications, and a strong track record in managing large-scale data systems or infrastructure.

How difficult is it to get hired at Google?

Getting hired for a Google Big Data role is competitive and typically requires strong technical skills in data engineering, proficiency with tools like Hadoop and Spark, and relevant experience or advanced education. Candidates often go through multiple interview rounds assessing technical knowledge, problem-solving, and cultural fit.
More about Google Big Data jobs
What states have the most Google Big Data jobs? States with the most job openings for Google Big Data jobs include:
What job categories do people searching Google Big Data jobs look for? The top searched job categories for Google Big Data jobs are:
Google Big Data jobs near you
Infographic showing various Google Big Data job openings in the United States as of July 2026, with employment types broken down into 2% As Needed, 67% Full Time, 28% Part Time, and 3% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
Data Engineer

Data Engineer

Stellantis

Auburn Hills, MI • On-site

$108K - $130K/yr

Full-time

Re-posted 14 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 128 frontline employees who took The Breakroom Quiz

15th of 44 rated automakers

Job description

The AI & Data Analytics Team is looking for a Senior Data Engineer to join our team. In this role, you will be responsible for designing, building, and optimizing robust data pipelines that process massive datasets in both batch and real-time. You will work at the intersection of software engineering and data science, ensuring that our data architecture is scalable, reliable, and follows industry best practices.
Priorities can change in a fast-paced environment like ours, so this role includes, but is not limited to, the following responsibilities:
  • Pipeline Development: Design and implement complex data processing pipelines using Apache Spark.
  • Architectural Leadership: Build scalable, distributed systems that handle high-throughput data streams and large-scale batch processing.
  • Infrastructure as Code: Manage and provision cloud infrastructure using Terraform.
  • CI/CD & Automation: Streamline development workflows by implementing and maintaining GitHub Actions for automated testing and deployment.
  • Code Quality: Uphold rigorous software engineering standards, including comprehensive unit/integration testing, code reviews, and maintainable documentation.
  • Collaboration: Work closely with stakeholders to translate business requirements into technical specifications.

Basic Qualifications:
  • Bachelors degree in Computer Science, Engineering, Mathematics, or a related technical discipline
  • A minimum of 5 years of experience in the data engineering and software development life cycle. Including:
    • A minimum of 4 years of hands-on experience in building and maintaining production data applications, current experience in both relational and columnar data stores.
    • A minimum of 4 years of hands-on experience working with AWS cloud services
  • Comprehensive experience with one or more programming languages such as Python, Java, or Rust
  • Comprehensive experience working with Big Data platforms (i.e., Spark, Google Big Query, Azure, AWS S3, etc.)
  • Familiarity with time series database, data streaming applications, event driven architectures, Kafka, Flink, and more
  • Experience with workflow management engines (i.e., Airflow, Luigi, Azure Data Factory, etc.)
  • Experience with designing and implementing real-time pipelines
  • Experience with data quality and validation
  • Experience with API design
  • Distributed Computing: Deep expertise in Apache Spark (Core, SQL, and Structured Streaming).
  • Programming Mastery: Strong proficiency in Scala or Java. You should be comfortable building production-grade applications in a JVM-based environment.
  • SQL Proficiency: Advanced knowledge of SQL for data transformation, analysis, and performance tuning.
  • DevOps & Tools: Hands-on experience with Terraform for infrastructure management and GitHub Actions for CI/CD pipelines.
  • Software Engineering Foundation: Solid understanding of data structures, algorithms, and design patterns. Experience applying "Clean Code" principles to data engineering.
  • Stream Processing: Experience with Apache Flink for low-latency stream processing.
  • Scripting: Proficiency in Python for automation, data analysis, or scripting.
  • Cloud Platforms: Experience with AWS, Azure, or GCP data services (e.g., EMR, Glue, Databricks).
  • Data Modeling: Familiarity with dimensional modeling, Lakehouse architectures (Delta Lake, Iceberg), or NoSQL databases.

Preferred Qualifications:
  • Comprehensive knowledge of relational database concepts, including data architecture, operational data stores, Interface processes, multidimensional modeling, master data management, and data manipulation
  • Expert knowledge and experience with custom ETL design, implementation and maintenance
  • Comprehensive experience designing, implementing, and iterating data pipelines using Big Data technologies
  • Certification in AWS or other cloud providers
  • Experience with Databricks notebook workflows
  • Experience with Terraform

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