Data Engineer-Data Platforms-Google
IBM · Bengaluru, Karnataka, India
- On site
- Full-time
- $120,000 / year
- Bengaluru, Karnataka, India
Job highlights
- Build data engineering solutions on Google Cloud.
- Design and develop batch/real-time data pipelines.
- Manage data platforms using Google services.
- Implement data migration and optimize data layers.
- Master's degree preferred for this role.
About the role
About IBM Consulting
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, Data Platforms
As a Data Engineer specializing in Google's data platforms, you will design, build, and maintain data engineering solutions on Google's Cloud ecosystem. This role requires expertise in utilizing various Google services for batch and real-time data pipelines, data migration, and data layer design. Your primary responsibilities will include:- Design Data Pipelines: Design and develop batch and real-time data pipelines for Data Warehouse and Datalake using Google services such as DataProc, DataFlow, PubSub, BigQuery, and Big Table.
- Develop Data Engineering Solutions: Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to build and maintain data engineering solutions.
- Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow), ensuring efficient data pipeline operations.
- Implement Data Migration: Develop and implement data migration solutions using Google services, ensuring seamless data transfer between systems.
- Optimize Data Layer: Design and optimize the data layer using Google services such as BigQuery, Big Table, and Cloud Spanner, ensuring efficient data storage and retrieval.
Preferred Education
Master's DegreeRequired Technical And Professional Expertise
- Google Cloud Ecosystem Expertise: Exposure to 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: Exposure to developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google services and open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala.
- Google Cloud Services Proficiency: Experience working with Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to build and maintain data engineering solutions.
- Data Platform Management Knowledge: Exposure to scheduling and managing the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) for efficient data pipeline operations.
- Data Layer Design Understanding: Experience working with data layer design using Google services such as BigQuery, Big Table, and Cloud Spanner for efficient data storage and retrieval.
Preferred Technical And Professional Experience
- Open Source Technologies: Exposure to utilizing open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala for developing and managing batch and real-time data pipelines.
- Data Migration Solutions: Experience working with Google services to develop and implement data migration solutions, ensuring seamless data transfer between systems.
- Cloud Composer Expertise: Exposure to using Cloud Composer (Airflow) for scheduling and managing the data platform, ensuring efficient data pipeline operations.
Key skills/competency
Data Engineer, Data Platforms, Google Cloud, Data Pipelines, BigQuery, DataFlow, PubSub, Big Table, Cloud Composer, Apache AirflowSkills & topics
- Data Engineer
- Data Platforms
- Google Cloud
- Data Pipelines
- BigQuery
- DataFlow
- PubSub
- Big Table
- Cloud Composer
- Apache Airflow
- Data Warehouse
- Datalake
- Data Migration
- Spark
- Python
- Scala
- IBM Consulting
- Master's Degree
How to get hired
- Tailor your resume: Highlight your Google Cloud ecosystem expertise and data pipeline development experience.
- Showcase Google services proficiency: Emphasize your skills with BigQuery, DataFlow, PubSub, and Big Table.
- Detail your platform management: Mention experience with Cloud Scheduler and Cloud Composer (Airflow).
- Prepare for technical interviews: Be ready to discuss data layer design and migration strategies.
- Align with IBM's culture: Demonstrate curiosity, collaboration, and a drive for innovation.
Technical preparation
Master Google Cloud data services (BigQuery, DataFlow).,Practice building batch and real-time pipelines.,Understand data modeling and database design.,Familiarize with Airflow and dbt for orchestration.
Behavioral questions
Describe a complex data challenge you solved.,How do you collaborate with cross-functional teams?,Tell me about a time you learned a new technology.,How do you approach optimizing data pipeline performance?
Frequently asked questions
- What specific Google Cloud services are essential for this Data Engineer role at IBM?
- For this Data Engineer, Data Platforms position at IBM, essential Google Cloud services include DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB. Proficiency in these is key for designing, building, and maintaining data engineering solutions.
- What level of experience is expected for data pipeline development in this IBM role?
- IBM expects candidates to have exposure to developing and managing both batch and real-time data pipelines for Data Warehouse and Datalake. This includes experience with Google services and open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala.
- Does IBM require prior experience with data migration for this Data Engineer position?
- While preferred, experience with developing and implementing data migration solutions using Google services is beneficial for this Data Engineer role at IBM, ensuring seamless data transfer between systems.
- What role does Apache Airflow play in this Data Engineer job at IBM?
- Apache Airflow, particularly through Google Cloud Composer, is important for scheduling and managing the data platform at IBM. Experience with Airflow is crucial for ensuring efficient data pipeline operations in this role.
- Is a Master's degree mandatory for the Data Engineer, Data Platforms role at IBM?
- A Master's degree is preferred for this Data Engineer, Data Platforms position at IBM. While not strictly mandatory, it indicates a strong educational background that aligns with the role's requirements.
- What are the primary responsibilities of a Data Engineer, Data Platforms at IBM?
- Key responsibilities include designing and developing data pipelines (batch/real-time), building data engineering solutions on Google Cloud, managing data platforms, implementing data migration, and optimizing data layers using services like BigQuery and Big Table.
- How does IBM Consulting foster career growth for its Data Engineers?
- IBM Consulting emphasizes long-term career development, encouraging curiosity, exploration of new ideas, and innovative solutions. They focus on growth and empathy, valuing unique skills and experiences to support your professional journey.