Data Engineer-Data Platforms-Google - 4

New York, NY

Actively hiring, first seen by us 3 days ago

Pay: Pay not listedSimilar roles state $127K–$190K
Work setting: Not stated
Employment: Not stated

What you’ll work on

Full posting

Design and build data pipelines using Google Dataproc, Dataflow, Pub/Sub and BigQuery
Develop batch and real-time pipelines for data warehouse and data lake
Schedule and manage the data platform with Cloud Scheduler and Cloud Composer (Airflow)
Build Spark, Hadoop and Apache Beam or Python data engineering solutions
Work with stakeholders to optimize data solutions for business objectives

From the employer’s posting

Design and build data pipelines using Google DataProc, DataFlow, PubSub, BigQuery, and other Google services to meet business requirements.

Develop and manage batch and real-time data pipelines for Data Warehouse and Datalake to ensure high-quality data delivery.

Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations.

Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to develop and manage data engineering solutions.

Collaborate with stakeholders to optimize data solutions and ensure alignment with business objectives.

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Posting record

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Actively hiringFirst seen by us 3 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Nov 1, 20263 days ago
Recorded sightings: 4
Last seen by us: Nov 4, 2026
Employer says posted: Oct 7, 2026

We found this posting on the National Labor Exchange. These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$127K–$190K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 2,133 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 3

Early. Similar jobs usually come down between day 6 and day 54.

From 10,782 similar postings that came down. Coming down is not always a hire.

IBM is hiring

272 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
Scala
BigQuery
dbt
Hadoop
Spark
Airflow
GCP
Google Cloud Storage

Where the posting names them

• Design Data Pipelines: Design and build data pipelines using Google DataProc, DataFlow, PubSub, BigQuery, and other Google services to meet business requirements. • Develop Data Engineering Solutions: Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to develop and manage data engineering solutions. • Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations.

• Google Cloud Ecosystem Expertise: Experience with designing, building, and maintaining data engineering solutions on Google's Cloud ecosystem, including services such as Google DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB. • Data Pipeline Development: Experience in developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python. • Open Source Technologies: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions.

• Data Pipeline Development: Experience in developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python. • Open Source Technologies: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions. • Data Platform Management: Experience in scheduling and managing the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations.

**Preferred technical and professional experience** • Open Source Proficiency: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions, enabling the development of efficient data pipelines and scalable data architectures. • Advanced Data Engineering: Experience with utilizing various data engineering tools and technologies to develop and manage data engineering solutions on Google's Cloud ecosystem, ensuring seamless integration and data flow.

Your primary responsibilities will include: • Design Data Pipelines: Design and build data pipelines using Google DataProc, DataFlow, PubSub, BigQuery, and other Google services to meet business requirements. • Develop Data Engineering Solutions: Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to develop and manage data engineering solutions.

**Required technical and professional expertise** • Google Cloud Ecosystem Expertise: Experience with designing, building, and maintaining data engineering solutions on Google's Cloud ecosystem, including services such as Google DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB. • Data Pipeline Development: Experience in developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python.

• Develop Data Engineering Solutions: Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to develop and manage data engineering solutions. • Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations. • Ensure Data Quality: Develop and manage batch and real-time data pipelines for Data Warehouse and Datalake to ensure high-quality data delivery.

• Open Source Technologies: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions. • Data Platform Management: Experience in scheduling and managing the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations. • Data Engineering Tools: Experience with utilizing various data engineering tools and technologies to develop and manage data engineering solutions on Google's Cloud ecosystem.

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Has IBM posted this job before?

Yes. We’ve followed one other Data Engineer-Data Platforms-Google - 4 job there since November 2026. It is still up.

San FranciscoStill up after 3 days, first seen Nov 1
This jobUp 3 days so far, first seen Nov 1

About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025: $139,500
Expected change, 2025 to 2035: 9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

**Introduction** A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences. **Your role and responsibilities** As a Data Engineer specializing in Google's data platforms, you will design, build, and maintain data engineering solutions on Google's Cloud ecosystem. You will utilize various Google services to develop batch and real-time data pipelines, manage data migration, and design data layers. Your primary responsibilities will include: • Design Data Pipelines: Design and build data pipelines using Google DataProc, DataFlow, PubSub, BigQuery, and other Google services to meet business requirements. • Develop Data Engineering Solutions: Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to develop and manage data engineering solutions. • Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations. • Ensure Data Quality: Develop and manage batch and real-time data pipelines for Data Warehouse and Datalake to ensure high-quality data delivery. • Optimize Data Solutions: Collaborate with stakeholders to optimize data solutions and ensure alignment with business objectives. This position can be performed from anywhere in the United States of America. **Required technical and professional expertise** • Google Cloud Ecosystem Expertise: Experience with designing, building, and maintaining data engineering solutions on Google's Cloud ecosystem, including services such as Google DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB. • Data Pipeline Development: Experience in developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python. • Open Source Technologies: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions. • Data Platform Management: Experience in scheduling and managing the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations. • Data Engineering Tools: Experience with utilizing various data engineering tools and technologies to develop and manage data engineering solutions on Google's Cloud ecosystem. **Preferred technical and professional experience** • Open Source Proficiency: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions, enabling the development of efficient data pipelines and scalable data architectures. • Advanced Data Engineering: Experience with utilizing various data engineering tools and technologies to develop and manage data engineering solutions on Google's Cloud ecosystem, ensuring seamless integration and data flow. • Data Platform Optimization: Experience in optimizing data solutions and ensuring alignment with business objectives, collaborating with stakeholders to drive business outcomes through data-driven insights. IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

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Pay: No pay amount identified in the saved description.
Location & working pattern: New York, NY
**Introduction** A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences. **Your role and responsibilities**
Work authorization: • Data Platform Optimization: Experience in optimizing data solutions and ensuring alignment with business objectives, collaborating with stakeholders to drive business outcomes through data-driven insights. IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

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