New opportunity

Data Engineer

Google · Hyderabad, India

About this role

The Data and Analytics Services (DAS) team at Google is seeking a Data Engineer to support the Platforms and Devices organization. You will be responsible for architecting, implementing, and optimizing complex, scalable data pipelines and managing centralized data warehouse components. The role focuses on building data foundations that support AI/ML initiatives, ensuring data quality, and driving AI-readiness. Responsibilities: Lead the design, development, and maintenance of ETL/ELT processes for the centralized data warehouse. Architect and optimize SQL queries for analytics and reporting. Develop data models specifically designed to support AI/ML initiatives and AI-motivated insights. Partner with executive stakeholders, data scientists, and AI teams to architect data solutions. Collaborate with other engineers to deliver solutions and promote technical growth. Minimum Qualifications: Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related quantitative field. 3 years of experience in Data Engineering, Data Infrastructure, or Data Analytics. Proficiency in Python or SQL. Experience managing data projects from conception to production and building pipelines for ML/AI workloads. Preferred Qualifications: Experience with Generative AI, LLM integrations, RAG, model fine-tuning, and vector databases. Expertise in GCP data services (BigQuery, Dataflow, Pub/Sub) and AI infrastructure (Vertex AI, BigQuery ML). Experience with advanced data modeling and schema design. Strong communication skills for translating technical concepts to non-technical audiences. Familiarity with consumer electronics or supply chain data is a plus.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLData EngineeringData InfrastructureData AnalyticsETLELTGoogle Cloud PlatformBigQueryDataflowPub/SubVertex AIBigQuery MLGenerative AILLMRAGData ModelingSchema Design

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Architecting AI-Ready Data Foundations
The role requires moving beyond standard ETL to build data models specifically optimized for AI/ML workloads. You must demonstrate how to structure data to support advanced initiatives like RAG, model fine-tuning, and vector databases, ensuring the infrastructure is scalable and performant for AI-driven insights.
End-to-End Data Warehouse Ownership
You are expected to own key components of the centralized data warehouse. This involves designing, implementing, and maintaining complex pipelines from conception to production, while upholding high standards for data quality, governance, and operational efficiency within the Platforms and Devices organization.
Cross-Functional Stakeholder Partnership
Success depends on your ability to translate complex business needs from executive stakeholders and data scientists into tangible technical solutions. You must be able to communicate technical concepts clearly to non-technical audiences to drive product strategy and operational decision-making.

Likely challenges to think through

Balancing Legacy Infrastructure with AI Innovation
You may face the difficulty of integrating modern AI/ML requirements into existing, large-scale data warehouse environments. Prepare by being ready to discuss strategies for migrating or augmenting legacy pipelines to support high-velocity AI workloads without compromising data integrity.
Managing High-Dimensional Data for AI Readiness
Building pipelines for AI/ML often involves handling unstructured or complex data types that differ from traditional reporting schemas. Prepare by articulating your approach to advanced data modeling and schema design that specifically addresses the unique requirements of vector databases and LLM integrations.

Practice your answers

Technical practice

Designing Scalable ETL/ELT Pipelines

What to coverDescribe your approach to building robust data pipelines on GCP. Focus on how you handle data partitioning, error handling, and monitoring to ensure reliability when processing massive datasets.

Optimizing SQL for Large-Scale Analytics

What to coverExplain your methodology for optimizing complex SQL queries in a data warehouse environment like BigQuery. Discuss techniques such as query plan analysis, partitioning, clustering, and minimizing data shuffling.

Data Modeling for AI/ML Workloads

What to coverHow do you design data schemas to support AI/ML initiatives? Address the differences between traditional analytical modeling and the requirements for features, training data, and inference-ready datasets.

Implementing RAG and Vector Databases

What to coverDiscuss the architectural considerations for integrating RAG (Retrieval-Augmented Generation) into a data pipeline. Focus on how you manage vector embeddings, indexing, and retrieval latency within a production environment.

GCP Data Services Integration

What to coverDescribe how you would orchestrate a workflow using Dataflow, Pub/Sub, and BigQuery. Explain the trade-offs between batch and streaming processing for real-time AI-driven insights.

Ensuring Data Quality and Governance

What to coverWhat strategies do you use to maintain high data quality and governance standards in a centralized data warehouse? Suggest focusing on automated testing, schema validation, and lineage tracking.

Fine-Tuning and Model Readiness

What to coverExplain the data engineering requirements for fine-tuning LLMs. Discuss how you prepare, clean, and version datasets to ensure they are suitable for model training and evaluation.

Managing Data Infrastructure Lifecycle

What to coverDescribe your experience taking a data project from initial conception to production. Highlight how you handle version control, CI/CD for data pipelines, and infrastructure as code.

Behavioural practice

Translating Technical Concepts to Stakeholders

What to coverProvide an example of a time you had to explain a complex technical data architecture to a non-technical executive. Focus on how you adapted your communication style to ensure alignment on business value.

Collaborating with Cross-Functional Teams

What to coverDescribe a situation where you worked closely with data scientists or AI researchers to deliver a solution. Explain how you balanced their specific data needs with the constraints of the existing data infrastructure.

Driving Technical Growth and Standards

What to coverGive an example of how you have influenced team standards or best practices. Focus on how you identified a gap in the current process and successfully advocated for a more efficient or scalable approach.

Handling Conflicting Project Priorities

What to coverDescribe a time you had to manage multiple data project requirements from different stakeholders. Explain how you prioritized tasks to meet deadlines while maintaining the integrity of the data platform.

Sharpen your knowledge

Topics to prepare

AI-Ready Data Architecture
Focus on designing data foundations that support ML workloads, specifically RAG and model fine-tuning. Exercise: Draft a schema design for a vector database integration that supports a hypothetical LLM application, ensuring data quality and lineage are maintained.
ETL/ELT Pipeline Optimization
Understand the trade-offs between ETL and ELT in the context of massive datasets. Exercise: Review a complex SQL transformation process and identify bottlenecks; propose a refactored approach using GCP-native services like Dataflow to improve scalability.

Skills to revise

GCP Data Services Proficiency
Mastery of BigQuery, Dataflow, and Vertex AI is critical for this role. Exercise: Build a small-scale pipeline in a GCP environment that ingests raw data, processes it via Dataflow, and loads it into BigQuery for analysis.
Advanced SQL and Python for Data Engineering
Focus on writing highly efficient, production-grade code for data manipulation. Exercise: Optimize a slow-running SQL query by analyzing execution plans and applying partitioning or clustering strategies in BigQuery.
Stakeholder Communication
The ability to translate technical data solutions for non-technical executive stakeholders. Exercise: Prepare a brief summary of a technical data project, explaining the business value and impact on AI-readiness without using jargon.

Your preparation checklist

Design a scalable AI-ready data pipeline
Prepare a technical walkthrough of an end-to-end ETL/ELT pipeline you have built. Focus on how you handled massive datasets, ensured data quality, and specifically structured the data to support AI/ML workloads or model fine-tuning.
Demonstrate GCP and AI infrastructure expertise
Review your experience with Google Cloud Platform services like BigQuery, Dataflow, and Pub/Sub. Be prepared to discuss how you have integrated these with Vertex AI or BigQuery ML to solve specific business problems.
Prepare examples of advanced data modeling
Document instances where you designed schemas for complex analytics. Explain your approach to data modeling for AI-readiness, including how you manage vector databases or RAG-based data structures.
Practice translating technical concepts for stakeholders
Prepare a summary of a past project where you collaborated with non-technical executive stakeholders. Focus on how you communicated the business value of your data infrastructure and translated complex technical requirements into actionable insights.
Formulate questions for the DAS team
Prepare thoughtful questions regarding the Platforms and Devices organization's current data challenges, such as how they prioritize AI-readiness initiatives or how the team maintains data governance standards across large-scale hardware and software datasets.

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