Data Engineer-Senior Software Engineer
Old Navy (Gap Inc.) (company site, opens in a new tab)4–7 yrsHyderabadRemoteDirect career pageEst. 15–28 LPA
Posted on 28 Sept
Reach people at Old Navy (Gap Inc.)
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Old Navy (Gap Inc.) you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, develop, and maintain cloud-native data pipelines and AI/ML-powered data products using Google Cloud Platform services.
- Build, deploy, and operationalize AI/ML models leveraging Vertex AI, BigQuery ML, and related GCP AI services to support business decision-making.
- Ensure data pipeline reliability and quality through code reviews, root cause analysis, and CI/CD automation within a DevOps environment.
Minimum Requirements
- 4–7 years of experience with at least 3 years specifically in Data Engineering or Cloud AI roles.
- Graduate degree in Computer Science, Engineering, or equivalent.
- Mandatory hands-on expertise with GCP data and AI services including BigQuery, Vertex AI, Cloud Dataflow/Dataproc, Cloud Composer, Cloud Storage, Pub/Sub, and Bigtable.
- Proficiency in Python, SQL, PySpark, Linux shell scripting, and experience with version control and CI/CD tools (GitHub, Cloud Build, Jenkins).
Ideal Candidate Profile
- Experienced in designing and operating scalable batch and streaming data pipelines in enterprise or large-scale environments using GCP.
- Skilled in building production-grade AI/ML workflows integrating Vertex AI and BigQuery ML, with operational experience in MLOps.
- Familiar with DevOps practices including automated deployments, monitoring, testing, and collaboration across global cross-functional teams.