AI Data Engineer
Corning Incorporated (company site, opens in a new tab)PuneOn-siteDirect career pageEst. 6–12 LPA
Posted on 31 Aug
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
Reach people at Corning Incorporated
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 Corning Incorporated you could write to:
- Recruiters & HR
- People in this function
- Work addresses
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
- Develop and maintain production-grade big-data ingestion pipelines supporting advanced analytics projects on on-premises and cloud data lakes.
- Collaborate with cross-organizational teams to define data ingestion, validation, cleansing, and enrichment requirements and ensure usable final datasets for domain experts and data scientists.
- Implement automated data validation, profiling, cleansing, and enrichment using agile development and CI/CD practices; participate in code reviews and DevOps support activities.
Minimum Requirements
- Bachelor’s degree in computer science, engineering, mathematics, or related technical discipline.
- Proficiency in at least one JVM language (Java, Scala, or Kotlin) and one interpreted declarative language such as Python.
- Hands-on experience with big data engineering concepts, ETL/ELT pipelines, and technologies like Apache Spark, S3, and Delta Lake.
- Entry-level experience with AWS platform services including S3, EC2, DMS, RDS, EMR, Redshift, Lambda, DynamoDB, CloudWatch, and CloudTrail.
Ideal Candidate Profile
- Candidate comfortable working in agile, CI/CD-driven environments focused on data pipeline development and automation.
- Experience collaborating across multiple teams including data source teams, domain experts, and data scientists to deliver validated, enriched data.
- Technical aptitude with modern big-data ecosystems, cloud services, and software engineering best practices suited for production analytics platforms.