JOBWAVE VERIFIED

AI GCP Data Engineer

Company: Not specified in the provided description
Job ID: 68333
Location: Chennai, Tamil Nadu, India
Experience: 3+ years
Preferred Experience: 5+ years
Education: Bachelor's degree or equivalent
Preferred Degree: Master of Engineering
Work Mode: Not specified
Employment Type: Full-time
Job Category: Enterprise Technology
Posting Date: October 8, 2026
Application Deadline: October 13, 2026, 12:00 AM (as listed in the posting)
Salary: Not specified
Core Domain: Cloud Data Engineering, AI/ML Data Infrastructure
Primary Stack: Python, SQL, Google Cloud Platform (GCP), BigQuery, Dataflow, Dataproc, Airflow/Cloud Composer, Dataform, dbt, Spark
Trending: Yes, according to the job posting

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JOBWAVE INSIGHTS

Who Is This Role For?

This role is suited to a Data Engineer with at least 3 years of hands-on experience building and maintaining production-grade ETL/ELT pipelines, cloud data platforms, and automated data workflows.

The ideal candidate can develop scalable data pipelines using Python and SQL, work with modern cloud data tools, maintain data quality, and prepare reliable datasets for analytics, machine learning, and generative AI applications.

The key differentiator: This is not just a traditional ETL role. The company is looking for someone who can build reliable data infrastructure that supports enterprise analytics and AI/GenAI use cases.

Key Skills

• Programming: Python, SQL
• Cloud: Google Cloud Platform (GCP), with AWS or Azure as alternatives
• Data Warehousing: BigQuery, data modeling, dimensional modeling
• Data Processing: Apache Spark, Dataflow, Dataproc
• Workflow Orchestration: Apache Airflow, Cloud Composer
• Data Transformation: Dataform, dbt
• Pipeline Engineering: ETL/ELT, batch processing, streaming pipelines
• Data Quality: Validation, monitoring, alerting, lineage, metadata management
• DataOps: Git, CI/CD, automated testing, version control, release management
• AI Data Engineering: Feature datasets, vector-ready datasets, document corpora, GenAI data preparation
• Optimization: Query performance, partitioning, clustering, storage and compute costs
• Governance: Privacy, access controls, compliance, secure data access

Priority Skills

Highest Priority

1. Python and SQL — Strong hands-on coding skills for data transformation, pipeline automation, validation, and production troubleshooting.
2. GCP and BigQuery — Understand cloud data architecture, analytical queries, storage, partitioning, clustering, and cost optimization.
3. ETL/ELT Pipeline Development — Build dependable ingestion, transformation, validation, and publishing workflows.
4. Data Processing Frameworks — Practical experience with Spark, Dataflow, Dataproc, or comparable tools.
5. Workflow Orchestration — Scheduling, dependencies, retries, failure handling, and monitoring with Airflow or Cloud Composer.
6. Data Modeling and Performance Tuning — Design efficient analytical data models and optimize pipeline execution.
7. Data Quality and Production Support — Implement validation, observability, alerting, lineage, and recovery mechanisms.

Strong Advantages

• Experience preparing datasets for GenAI, RAG, semantic search, or ML feature engineering.
• Handling unstructured documents, logs, text, images, transcripts, and embeddings.
• Hands-on DataOps, CI/CD, automated pipeline testing, and production incident resolution.
• Migration from Hadoop, Alteryx, or on-premises data platforms to cloud services.
• Streaming ingestion, event-driven architecture, APIs, and real-time analytics.
• Cloud certifications and experience mentoring junior engineers.

What to Prepare

• Build or explain an end-to-end GCP data pipeline.
• Practise advanced SQL, joins, window functions, aggregations, and query optimization.
• Demonstrate Python-based data processing and error handling.
• Explain BigQuery partitioning, clustering, and query-cost reduction.
• Understand batch versus streaming processing and when to use each.
• Prepare an Airflow DAG example with dependencies, retries, and failure notifications.
• Explain data validation, schema changes, duplicate handling, and late-arriving data.
• Learn how data engineering supports RAG, vector search, and AI applications.
• Prepare real project examples covering performance improvements, reliability, and cost savings.

What to Focus On

1. Python and SQL

Be prepared to write clean, modular code and explain how you handle large datasets, null values, duplicates, invalid records, incremental loads, and failed transformations.

2. GCP Data Architecture

Prioritize BigQuery, Dataflow, Dataproc, and Cloud Composer. Understand which service fits a given workload and how data moves between ingestion, processing, storage, and consumption layers.

3. Pipeline Reliability

Know how to implement retries, idempotency, checkpoints, schema validation, logging, alerting, backfills, and recovery from partial failures.

4. Data Quality and Governance

Understand how to enforce data contracts, track lineage, monitor freshness, manage access permissions, and protect sensitive data.

5. AI and GenAI Data Enablement

Understand how to clean and prepare documents, split text into chunks, generate embeddings, maintain metadata, and create datasets suitable for retrieval-augmented generation (RAG).

6. DataOps and Production Engineering

Be able to explain how you use Git, CI/CD, automated tests, code reviews, deployment workflows, monitoring, and incident management to operate pipelines safely.

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JOB DETAILS

Original Role Information

Role Overview

The organization is seeking a hands-on Data Engineer to build production-ready data pipelines and cloud data platforms. The engineer will work with structured, semi-structured, and unstructured data while ensuring quality, security, lineage, reliability, and cost efficiency.

The role also involves preparing trusted data assets for business intelligence, analytics, machine learning, and GenAI-enabled applications.

Responsibilities

Data Pipeline Development

• Translate business, analytics, and AI requirements into scalable engineering solutions.
• Design and maintain batch and streaming pipelines.
• Implement ingestion, transformation, validation, and publishing workflows.
• Develop reusable, well-documented data products.

Cloud Data Platform Engineering

• Work with BigQuery, Dataflow, Dataproc, Cloud Composer/Airflow, Dataform, dbt, Spark, or equivalent technologies.
• Build resilient cloud-native data workflows.
• Optimize query execution, pipeline performance, storage, and compute costs.

Data Quality and Governance

• Implement data quality checks, observability, monitoring, metadata management, and lineage.
• Establish appropriate privacy controls, access policies, and compliance practices.
• Improve trust in enterprise data assets.

AI/ML Data Enablement

• Prepare high-quality datasets for ML models and feature engineering.
• Build vector-ready datasets and document corpora for GenAI and semantic search.
• Support RAG-enabled applications and governed data access.
• Collaborate with data scientists, ML engineers, product owners, and business stakeholders.

DataOps and Production Support

• Apply CI/CD, version control, automated testing, and release management.
• Follow reusable engineering standards and documentation practices.
• Support production deployments, troubleshooting, and incident resolution.
• Stay updated on cloud data engineering and AI infrastructure technologies.

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REQUIREMENTS

Experience

• Minimum 3+ years of experience in data engineering, ETL/ELT development, data warehousing, or cloud data platform delivery.
• Preferred 5+ years of enterprise cloud data engineering experience.
• Experience operating scalable data pipelines in production.
• Familiarity with agile development, code reviews, documentation, and production support.

Programming

• Strong Python skills.
• Strong SQL skills.
• Modular, maintainable, and testable code.
• Data transformation, automation, validation, and debugging.

Cloud and Data Platforms

• GCP, AWS, Azure, or equivalent enterprise cloud ecosystem.
• BigQuery, Spark, Dataflow, Dataproc, Airflow/Cloud Composer, Dataform, dbt, or similar tools.
• Cloud-native pipeline design and operation.
• Data warehousing and analytical data processing.

Data Engineering Concepts

• ETL/ELT architecture.
• Batch and streaming pipelines.
• Data ingestion, transformation, and publishing.
• Data modeling and dimensional modeling.
• Partitioning, clustering, and performance tuning.
• Storage and compute cost optimization.
• Data quality frameworks and production monitoring.

DataOps and Governance

• Git and version control.
• CI/CD and automated testing.
• Pipeline observability, alerting, and incident resolution.
• Metadata, data lineage, access control, privacy, and compliance.
• Documentation and reusable engineering standards.

AI/ML and GenAI — Preferred

• Feature engineering and ML-ready datasets.
• GenAI data preparation and document processing.
• Semantic search and retrieval-augmented generation.
• Vector-ready datasets and embeddings.
• MLOps enablement and model monitoring.
• Handling structured, semi-structured, and unstructured data.

Additional Preferred Experience

• Migrating Hadoop, Alteryx, or on-premises workflows to cloud platforms.
• API integration, microservices, event-driven systems, and streaming analytics.
• Cloud certifications.
• Mentoring engineers and defining engineering standards.

Soft Skills

• Clear communication with technical and business stakeholders.
• Translating business needs into technical solutions.
• Collaboration with analytics, product, and AI/ML teams.
• Problem-solving and ownership of production systems.

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QUALIFICATIONS

• Bachelor's or master's degree in Computer Science, Data Engineering, Information Systems, Engineering, Statistics, Mathematics, or a related technical field.
• A bachelor's degree or equivalent is listed as the minimum education requirement.
• A Master of Engineering is listed as the preferred degree.
• At least 3 years of relevant hands-on data engineering experience.
• Strong Python and SQL proficiency.
• Experience with cloud-based data platforms and modern data engineering tools.
• Understanding of data quality, governance, testing, monitoring, and performance optimization.

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POTENTIAL GAPS TO CHECK

Use these points to evaluate your fit before applying:

• GCP depth: Have you worked directly with BigQuery, Dataflow, Dataproc, or Cloud Composer, rather than only studying them?
• Production experience: Can you explain a pipeline you built, deployed, monitored, and supported?
• SQL proficiency: Can you solve complex SQL problems and explain query optimization?
• Python engineering: Can you demonstrate maintainable code, automated tests, error handling, and large-scale data processing?
• Orchestration: Have you managed dependencies, retries, backfills, and failure recovery?
• Data quality: Can you explain how you detect missing, duplicated, stale, or invalid data?
• AI/GenAI experience: Can you explain how you would prepare enterprise documents and metadata for RAG or semantic search? This is preferred, not a stated minimum requirement.
• Cloud migration: Have you migrated or modernized legacy pipelines? This is also preferred rather than mandatory.

Important: The minimum requirement is 3+ years. The 5+ years requirement appears under preferred qualifications, so candidates with 3–4 years may still be suitable if they demonstrate strong hands-on engineering experience.

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INTERVIEW PREPARATION

Phase 1 — Core Python and SQL

• Practise joins, CTEs, window functions, aggregations, and deduplication.
• Write Python functions for parsing, validating, and transforming data.
• Handle null values, malformed records, exceptions, and schema changes.
• Explain unit testing and how you make transformation logic reusable.
• Solve incremental-load and change-data-capture scenarios.

Phase 2 — GCP and Data Engineering

• Explain BigQuery architecture and analytical query execution.
• Practise designing tables with appropriate partitioning and clustering.
• Compare Dataflow, Dataproc, and BigQuery for different workloads.
• Design an Airflow DAG with dependencies, retries, and alerting.
• Explain batch processing versus streaming and how to select an approach.
• Discuss cost optimization, workload performance, and data retention.

Phase 3 — Production Pipelines and DataOps

• Design a pipeline from source ingestion to curated analytical tables.
• Explain how you detect and recover from failed jobs.
• Discuss idempotency, retries, checkpoints, and backfills.
• Design automated data quality tests and freshness monitoring.
• Explain CI/CD for data pipelines, environment configuration, and rollback strategies.
• Prepare a real example of resolving a production data issue.

Phase 4 — AI/ML and GenAI Data Engineering

• Explain how to process documents into clean, searchable datasets.
• Understand document chunking, metadata, embeddings, and vector search.
• Describe how a data pipeline can support a RAG application.
• Discuss permissions and governance when AI applications retrieve enterprise data.
• Explain how data quality and freshness affect downstream AI results.
• Review feature datasets and the importance of reproducible data preparation.

Final Interview Focus

Prepare clear, project-based answers to these questions:

1. Describe a production data pipeline you designed. What was the architecture, scale, and outcome?
2. How would you build an end-to-end data pipeline on GCP?
3. When would you use BigQuery, Dataflow, or Dataproc?
4. How do you optimize a slow or expensive BigQuery query?
5. How do you implement incremental ingestion without duplicating records?
6. How would you handle late-arriving events and schema changes?
7. How do you monitor data freshness, quality, and pipeline failures?
8. How would you orchestrate dependent workflows using Airflow or Cloud Composer?
9. How would you prepare enterprise documents for a RAG-based AI application?
10. How do you secure sensitive data while making it available to analytics and AI teams?
11. Describe a difficult production issue you diagnosed and resolved.
12. Give an example of reducing pipeline runtime, cloud cost, or operational failures.

Recommended preparation order: Python and SQL → BigQuery and GCP architecture → ETL/ELT and orchestration → data quality and DataOps → AI/GenAI data preparation.

The strongest application will demonstrate real production ownership, not just familiarity with cloud service names. Prepare at least two detailed project examples with architecture, challenges, design decisions, measurable results, and lessons learned.