Corning is hiring an AI Data Engineer in Pune to support the development of reliable, production-grade data pipelines for advanced analytics platforms. This role involves working with engineering teams, domain experts, data scientists, and application teams to ingest, validate, cleanse, enrich, and manage data from multiple operational sources. Candidates will gain exposure to enterprise-scale big data systems, cloud platforms, Apache Spark, AWS services, data lakes, CI/CD practices, and modern DevOps workflows. It is a strong opportunity for technically skilled candidates interested in building a long-term career in data engineering and large-scale analytics infrastructure.
This AI Data Engineer opportunity at Corning focuses on one of the most important foundations of modern analytics: delivering reliable, validated, and usable data to the teams that depend on it. Rather than working only with reports or isolated datasets, the selected candidate will contribute to production data pipelines that support advanced analytics across a large global organization.
⚙️ Building Production Data Pipelines
The central responsibility in this role is developing and maintaining big-data ingestion pipelines. These pipelines bring information from different process and operational systems into on-premises and cloud-based data lake environments.
The work involves designing, testing, deploying, and maintaining data flows that can operate reliably in production. Candidates will work with established engineering frameworks and development practices while collaborating closely with senior technical professionals.
A typical pipeline may involve collecting source data, validating its structure and quality, transforming it where necessary, and landing it in a format that downstream analytics teams can use efficiently.
Production Data Engineering Environment
Technologies relevant to this work include Apache Spark AWS S3 Parquet and Delta Lake .
🔍 Data Quality And Validation
Moving data is only one part of data engineering. Corning's role also emphasizes automated validation and data profiling.
Candidates will help ensure that incoming data meets expected quality and structural requirements. This can include identifying missing values, unexpected formats, inconsistent records, schema changes, or other issues that could affect analytics and machine learning workloads.
The engineering team works with data source owners to define what valid data should look like and then develops automated mechanisms to check those expectations.
Reliable analytics begins with reliable data, which makes validation and profiling essential parts of production data engineering.
This exposure is particularly valuable for candidates who want to understand how enterprise organizations maintain trust in large and continuously changing datasets.
☁️ Enterprise Cloud Exposure
The position provides entry-level exposure to several important AWS services used in modern data platforms.
Candidates should understand or be prepared to work with services such as:
AWS S3 for data storage, EC2 for computing infrastructure, DMS for database migration, RDS for managed relational databases, and EMR for large-scale data processing.
The role also mentions exposure to Amazon Redshift , Lambda , DynamoDB , CloudWatch , and CloudTrail .
Cloud Data Engineering Enterprise Systems
Understanding how these services interact within a broader data architecture can help candidates build practical cloud engineering knowledge beyond theoretical certification concepts.
🧹 Cleansing And Enriching Data
Once data has been successfully landed in the platform, it may need additional processing before it becomes useful to data scientists and business teams.
The AI Data Engineer will work with domain experts and technical teams to understand requirements for data cleansing and enrichment. This means transforming raw operational information into datasets that are more consistent, complete, and directly usable.
For example, raw source data may contain inconsistent formats or incomplete business attributes. Engineering processes can standardize these records and add useful contextual information before the data reaches analytics users.
The objective is to reduce unnecessary downstream processing and provide datasets that are ready for practical use.
🧠 Programming Expectations
Corning expects candidates to have production programming knowledge in at least one modern JVM language.
Relevant languages include:
Java • Scala • Kotlin
Python is also an important part of the technical profile because it is widely used across data engineering, analytics automation, and data science environments.
JVM Language + Python Knowledge
Candidates should be comfortable understanding structured code, debugging technical issues, and working with collaborative software development practices.
🔄 Engineering And DevOps Workflow
This is not purely an analytics-focused role. The position operates within modern software engineering and DevOps practices.
Candidates will participate in activities such as code reviews, version control, automated deployment workflows, and system monitoring.
Useful tools and concepts include:
Git • GitLab • Jira • Terraform • New Relic
The role also includes supporting production environments by monitoring tokens, jobs, and overall system performance.
Manual Data Processing Automated Production Pipelines
This type of experience can be valuable because modern data engineers increasingly need to understand both data processing and software delivery practices.
🤝 Working Across Technical Teams
The AI Data Engineer will collaborate with several groups across the organization. These may include platform developers, data source teams, application developers, controls engineers, domain experts, and data scientists.
Communication is therefore an important part of the role.
Candidates should be able to collect requirements, explain technical decisions, and discuss how data models or datasets will be used. Strong communication helps ensure that engineering solutions match actual business and operational requirements.
The strongest candidates for enterprise data engineering roles are usually able to combine technical execution with clear communication across different teams.
🧭 What Recruiters May Evaluate
For this position, candidates can expect technical evaluation around core data engineering concepts rather than only theoretical definitions.
Important preparation areas include understanding how ETL and ELT pipelines work, basic Apache Spark architecture, distributed data processing, cloud storage, relational databases, and different data formats.
Candidates should also understand the purpose of Parquet and Delta Lake and how data lakes support large-scale analytics workloads.
Possible interview preparation areas include:
Explaining a data pipeline you have built or studied
Understanding Spark components and distributed processing
Working with structured, semi-structured, and unstructured data
Python programming and data transformation logic
SQL and relational database fundamentals
AWS service fundamentals
Git and version control workflows
Data quality and validation concepts
ETL versus ELT architecture
📚 Skills Worth Strengthening
Candidates preparing specifically for this opportunity should focus on building practical knowledge rather than collecting only theoretical certifications.
A strong learning combination would include Python programming, SQL, Apache Spark, cloud fundamentals, and data pipeline development.
Working on a project where data moves from a source system into cloud storage, gets processed through Spark, validated for quality, and stored in an analytics-ready format would provide relevant practical exposure.
Notebook environments such as JupyterHub are also relevant because they are commonly used for collaborative technical experimentation and analytics development.
📄 Resume Strengthening Tips
Candidates should clearly demonstrate practical data engineering exposure on their resumes.
Instead of simply listing technologies, describe what you built using them.
For example, a stronger project description would explain that you developed a pipeline to ingest structured data, transformed it using Apache Spark, applied validation rules, and stored the processed output in a cloud-based data environment.
Projects involving Python , PySpark , SQL , AWS , or data lake technologies can be particularly relevant.
Recruiters will also notice experience with collaborative development practices, including Git repositories, documentation, testing, and deployment workflows.
🔑 Keywords for Resume
Python • Java • Scala • Kotlin • SQL • Apache Spark • PySpark • ETL • ELT • Data Engineering • Big Data • AWS S3 • EC2 • AWS EMR • AWS Lambda • Amazon Redshift • DynamoDB • AWS RDS • AWS DMS • CloudWatch • CloudTrail • Parquet • Delta Lake • Data Lakes • Data Validation • Data Profiling • Data Cleansing • Data Enrichment • Git • GitLab • CI/CD • Terraform • Jira • New Relic • JupyterHub • Agile Development • DevOps • Data Modeling • Relational Databases
💡 Final Career Perspective
This Corning AI Data Engineer role is particularly relevant for candidates looking to build experience in enterprise-scale data engineering rather than working only with small standalone projects. The combination of Apache Spark, AWS services, data lakes, automated validation, CI/CD, and cross-functional collaboration provides exposure to the technical areas commonly expected in modern data engineering careers. Candidates with strong programming fundamentals and practical projects involving data pipelines can align their profiles effectively with this opportunity.
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