Job Description

What do you drive for us?

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● 2-10 years of experience as a Data Engineer or similar role:

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Proven track record of designing and implementing data solutions on Google Cloud

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Platform.

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As a Data Engineer at Randstad, you will play a pivotal role in designing, implementing, and

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maintaining our data infrastructure on Google Cloud Platform. You will work closely with our business

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intelligence, engineering, and business teams to ensure that our data is collected, stored, processed,

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and analyzed effectively, enabling us to make informed decisions and drive impactful outcomes.

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What a typical day at work would look like?

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● Collaborate with stakeholders: Engage in discussions with business users, BI

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analysts, data engineers to understand their data needs and translate them into

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technical solutions.

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● Design and implement data pipelines: Architect and build data pipelines using GCP

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services like Cloud Dataflow, Cloud Dataproc, and BigQuery to ensure efficient data

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ingestion, transformation, and delivery.

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● Develop data models: Design and implement data models in BigQuery, considering

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performance, scalability, and security requirements.

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● Manage and optimize data storage: Manage data storage on Google Cloud Storage,

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optimizing for cost-efficiency and performance.

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● Ensure data quality: Implement data validation and quality checks throughout the data

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pipeline to ensure data accuracy and integrity.

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● Troubleshoot and resolve issues: Diagnose and solve complex data-related issues, ensuring

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smooth data flow and optimal performance.

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● Stay updated on GCP advancements: Keep abreast of the latest Google Cloud Platform

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offerings and best practices to continually enhance our data solutions.

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Your Key Knowledge Areas shall be:

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● Deep understanding of GCP data services: Expertise in using GCP services such as Cloud

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Dataflow, Cloud Dataproc, BigQuery, Cloud Storage, Data Catalog, and Data Fusion. ● Data

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modeling and architecture: Proven experience designing and implementing data models,

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including dimensional modeling and data warehousing principles.

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● Data warehousing and data lakes: Robust understanding of data warehousing concepts, data

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lake architectures, and best practices for data management.

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● Data pipeline development: Proficiency in building data pipelines using tools like Apache

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Beam, Apache Spark, or other relevant technologies.

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● SQL and Python programming: Strong command of SQL for data querying and manipulation,

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and proficiency in Python for data analysis and automation.

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● Data governance and security: Understanding of data governance principles, data security

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best practices, and compliance requirements.

📌 Data Engineer (Hyderabad)
🏢 Randstad Global Capability Center
📍 Hyderabad