Data Engineer-Data Platforms-Google - 3

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 develop batch and real-time data pipelines for warehouse and data lake
Build data engineering solutions with Dataproc, Dataflow, BigQuery and Bigtable
Schedule and manage data platform jobs with Cloud Scheduler and Cloud Composer (Airflow)
Design and optimize the data layer for efficient migration and processing
Ensure scalability and efficiency of pipelines to meet business needs

From the employer’s posting

Design and develop batch and real-time data pipelines for Data Warehouse and Datalake

Build and maintain data engineering solutions using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, Google DataFlow with Apache Beam or Python

Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow)

Design and optimize the data layer for efficient data migration and data processing

Ensure scalability and efficiency of data pipelines and data engineering solutions to meet business needs

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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 6, 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 10,805 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

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Python
Scala
BigQuery
dbt
Hadoop
Spark
Airflow
GCP
Google Cloud Storage

Where the posting names them

• Design Data Pipelines: Design and develop batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud services such as DataProc, DataFlow, PubSub, BigQuery, and Big Table. • Develop Data Engineering Solutions: Build and maintain data engineering solutions using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, Google DataFlow with Apache Beam or Python, and other open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala. • Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow), ensuring seamless data pipeline operations.

• Deep Expertise in Google Data Platforms: Proven experience designing, building, and maintaining data engineering solutions on Google's Cloud ecosystem, including Google DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB. • Proficiency in Open-Source Technologies: Experience with Apache Beam, Apache Airflow, dbt, Spark/Python, or Spark/Scala, and ability to integrate these technologies with Google Cloud services. • Batch and Real-Time Data Pipelines: Experience developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud services.

• dbt and Data Modeling: Familiarity with dbt and data modeling concepts, including data warehousing and data lake architecture, is beneficial for designing and optimizing data layers. • Spark and Scala Expertise: Proficiency in Spark and Scala, including integrating them with Google Cloud services such as DataProc, is desirable for building and maintaining data engineering solutions. 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.

Your primary responsibilities will include: • Design Data Pipelines: Design and develop batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud services such as DataProc, DataFlow, PubSub, BigQuery, and Big Table. • Develop Data Engineering Solutions: Build and maintain data engineering solutions using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, Google DataFlow with Apache Beam or Python, and other open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala.

**Required technical and professional expertise** • Deep Expertise in Google Data Platforms: Proven experience designing, building, and maintaining data engineering solutions on Google's Cloud ecosystem, including Google DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB. • Proficiency in Open-Source Technologies: Experience with Apache Beam, Apache Airflow, dbt, Spark/Python, or Spark/Scala, and ability to integrate these technologies with Google Cloud services.

• Advanced Apache Beam Knowledge: Experience with Apache Beam, including integrating it with Google Cloud services such as DataFlow, is highly valued. Ability to optimize Beam pipelines for scalability and efficiency is a plus. • dbt and Data Modeling: Familiarity with dbt and data modeling concepts, including data warehousing and data lake architecture, is beneficial for designing and optimizing data layers. • Spark and Scala Expertise: Proficiency in Spark and Scala, including integrating them with Google Cloud services such as DataProc, is desirable for building and maintaining data engineering solutions.

• Develop Data Engineering Solutions: Build and maintain data engineering solutions using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, Google DataFlow with Apache Beam or Python, and other open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala. • Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow), ensuring seamless data pipeline operations. • Optimize Data Layer: Design and optimize the data layer for efficient data migration and data processing using Google Cloud services.

• Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow), ensuring seamless data pipeline operations. • Optimize Data Layer: Design and optimize the data layer for efficient data migration and data processing using Google Cloud services. • Ensure Scalability: Ensure scalability and efficiency of data pipelines and data engineering solutions to meet business needs.

• Proficiency in Open-Source Technologies: Experience with Apache Beam, Apache Airflow, dbt, Spark/Python, or Spark/Scala, and ability to integrate these technologies with Google Cloud services. • Batch and Real-Time Data Pipelines: Experience developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud services. • Data Platform Management: Experience scheduling and managing data platforms using Google Cloud Scheduler and Cloud Composer (Airflow).

• Batch and Real-Time Data Pipelines: Experience developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud services. • Data Platform Management: Experience scheduling and managing data platforms using Google Cloud Scheduler and Cloud Composer (Airflow). • Data Layer Optimization: Experience designing and optimizing data layers for efficient data migration and data processing using Google Cloud services.

• Data Platform Management: Experience scheduling and managing data platforms using Google Cloud Scheduler and Cloud Composer (Airflow). • Data Layer Optimization: Experience designing and optimizing data layers for efficient data migration and data processing using Google Cloud services. **Preferred technical and professional experience**

**Preferred technical and professional experience** • Advanced Apache Beam Knowledge: Experience with Apache Beam, including integrating it with Google Cloud services such as DataFlow, is highly valued. Ability to optimize Beam pipelines for scalability and efficiency is a plus. • dbt and Data Modeling: Familiarity with dbt and data modeling concepts, including data warehousing and data lake architecture, is beneficial for designing and optimizing data layers.

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Has IBM posted this job before?

Yes. We’ve followed one other Data Engineer-Data Platforms-Google - 3 job there since November 2026. It is still up.

San FranciscoStill up after 4 days, first seen Nov 1
This jobUp 4 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 seasoned 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 your expertise in Google's services and open-source technologies to deliver scalable and efficient data pipelines. Your primary responsibilities will include: • Design Data Pipelines: Design and develop batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud services such as DataProc, DataFlow, PubSub, BigQuery, and Big Table. • Develop Data Engineering Solutions: Build and maintain data engineering solutions using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, Google DataFlow with Apache Beam or Python, and other open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala. • Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow), ensuring seamless data pipeline operations. • Optimize Data Layer: Design and optimize the data layer for efficient data migration and data processing using Google Cloud services. • Ensure Scalability: Ensure scalability and efficiency of data pipelines and data engineering solutions to meet business needs. This role can be performed from anywhere in the United States of America **Required technical and professional expertise** • Deep Expertise in Google Data Platforms: Proven experience designing, building, and maintaining data engineering solutions on Google's Cloud ecosystem, including Google DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB. • Proficiency in Open-Source Technologies: Experience with Apache Beam, Apache Airflow, dbt, Spark/Python, or Spark/Scala, and ability to integrate these technologies with Google Cloud services. • Batch and Real-Time Data Pipelines: Experience developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud services. • Data Platform Management: Experience scheduling and managing data platforms using Google Cloud Scheduler and Cloud Composer (Airflow). • Data Layer Optimization: Experience designing and optimizing data layers for efficient data migration and data processing using Google Cloud services. **Preferred technical and professional experience** • Advanced Apache Beam Knowledge: Experience with Apache Beam, including integrating it with Google Cloud services such as DataFlow, is highly valued. Ability to optimize Beam pipelines for scalability and efficiency is a plus. • dbt and Data Modeling: Familiarity with dbt and data modeling concepts, including data warehousing and data lake architecture, is beneficial for designing and optimizing data layers. • Spark and Scala Expertise: Proficiency in Spark and Scala, including integrating them with Google Cloud services such as DataProc, is desirable for building and maintaining data engineering solutions. 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: • Spark and Scala Expertise: Proficiency in Spark and Scala, including integrating them with Google Cloud services such as DataProc, is desirable for building and maintaining data engineering solutions. 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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