Data Engineer-Senior Software Engineer

About the RoleWe are hiring a Senior Software Engineer (G6) to build and deliver intelligent, cloud-native data solutions on Google Cloud Platform (GCP) at Gap Inc. In this role, you will develop robust data pipelines and AI/ML-powered applications that enable smarter decision-making across our global retail operations. You will work hands-on with GCP-native services and modern AI frameworks, collaborate closely with cross-functional teams, and contribute to engineering best practices within GapTech's growing Hyderabad hub.
This role is ideal for engineers passionate about cloud data engineering and applied AI who want to build production-grade systems that create real business impact for iconic global brands.What You'll Do
Design, develop, and maintain GCP-based data pipelines integrating multiple business applications and data sources.
Build and deploy AI/ML models and data products using GCP AI services (Vertex AI, BigQuery ML, Gemini APIs).
Develop data transformation logic using PySpark, SQL, and Python on GCP platforms (Dataflow, Dataproc, BigQuery).
Work within a DevOps environment with CI/CD pipelines, automated deployments, and cloud-native monitoring.
Participate in code reviews, uphold coding standards, and ensure data quality and pipeline reliability.
Perform root cause analysis on data issues and implement robust, long-term solutions.
Support business stakeholders with data requirements, analytics insights, and reporting needs.
Contribute to improving automation, testing, and observability practices across the data platform.
Who You Are
4–7 years of overall experience, with at least 3+ years in a Data Engineering or Cloud AI role.
Graduate degree in Computer Science, Engineering, or equivalent.
Mandatory: Hands-on expertise with GCP data and AI services:
BigQuery — data modeling, optimization, partitioning, and clustering
Vertex AI — model training, deployment, and MLOps pipelines
Cloud Dataflow / Dataproc — batch and streaming data processing
Cloud Composer (Apache Airflow) — pipeline orchestration
Cloud Storage, Pub/Sub, and Bigtable — data storage and real-time streaming
Mandatory: Experience building and operationalizing AI/ML models in GCP (Vertex AI, BigQuery ML, or Generative AI APIs).
Proficiency in Python and SQL for data engineering and AI/ML workflows.
Familiarity with PySpark for large-scale data transformations.
Strong understanding of data pipeline architecture — batch, micro-batch, and streaming patterns.
Experience with version control and CI/CD tools (GitHub, Cloud Build, Jenkins).
Proficiency in Linux shell scripting and automation.
Good-to-Have Skills
Experience with LLM integration or Generative AI on GCP (Gemini APIs, Model Garden, or similar).
Familiarity with data governance, data cataloging (Dataplex), and data lineage on GCP.
Exposure to dbt (data build tool) for transformation and documentation.
Knowledge of enterprise integration patterns and event-driven architectures.
Experience with BI and reporting tools such as Looker, Power BI.
Strong communication and stakeholder collaboration skills in cross-functional, global teams.