AI Data Engineer

Corning — Pune, Maharashtra

₹8–14 LPA (HireSetu estimate) · Entry level · Freshers eligible · Full-time · onsite

HireSetu listing reference 80ad91 · employer requisition MH-410501 — confirm requirements below, then apply on the employer site.

Quick answer: Strip the title back and this is big-data ingestion: pipelines that land process and operational data into on-prem and cloud data lakes, with profiling and validation wired in from the start. The requirement that narrows the field is production proficiency in a JVM language and in Python, both at once.

From Corning's job posting

AI Data Engineer

Date: Aug 31, 2026

Location

Pune, MH, IN, 410501

Corning

Requisition Number: 77869

Join us.

Corning is one of the world’s leading innovators in glass, ceramic, and materials science. From the depths of the ocean to the farthest reaches of space, our technologies push the boundaries of what’s possible.

At Corning, there are endless possibilities for making an impact. You can help connect the unconnected, drive the future of automobiles, transform at-home entertainment, and ensure the delivery of lifesaving medicines. And so much more.

Come break through with us.

Corning’s businesses are ever-evolving to best serve our customers, industries, and consumers. Today, we accelerate and transform life sciences, mobile consumer electronics, optical communications, display, automotive, and solar markets. We are changing the world with:

Trusted products that accelerate drug discovery, development, and delivery to save lives
Damage-resistant cover glass to enhance the devices that keep us connected
Optical fiber, wireless technologies, and connectivity solutions to carry information and ideas at the speed of light
Precision glass for advanced displays to deliver richer experiences
Auto glass and ceramics to drive cleaner, safer, and smarter transportation
Solar polysilicon, wafers, and innovative photovoltaic modules, enabling low-cost solar energy solutions

Overview

The Data Engineer, Advanced Analytics platforms will work with our core platform development team as well as domain experts, application developers, controls engineers and data scientists. Their primary responsibility will be to develop reliable and instrumented data ingestion pipelines that land inbound data from multiple process and operational data stores throughout the company to on-premises and cloud-based data lakes. These pipelines will require data validation and data profiling automation along with version control and CI/CD to ensure ongoing resiliency and maintainability of the inbound data flows supporting our advanced analytics projects.

Responsibilities

As a data engineer for our advanced analytics platforms, your main responsibilities will be:

Design, test, deploy and maintain production big-data ingestion pipelines using established frameworks, patterns of practice, agile software development and CI/CD practices, working closely with the Principal Software Engineer – Data Ingestion
Work with cross-organizational data source teams to define data ingestion requirements for structured, unstructured and semi-structured data, pilot their implementation, ensure the data source teams accept the resulting landed data as valid
Define and implement automated validation and profiling capabilities needed to ensure reliable data delivery, using agile software development and CI/CD practices
Work with data source teams, domain experts and data scientists to define data cleansing and data enrichment requirements for landed data
Implement data cleansing and enrichment code using established patterns of practice General - Corning (L4)
Work with data source teams, domain experts and data scientists to validate landed, cleansed and enriched data, using agile software development and CI/CD practices, while ensuring that the final datasets are directly usable by them without additional processing effort
Actively participate in code reviews and technical information sharing with your team members and the broader software engineering community at Corning
Stay up to date with industry standards and technological advancements that will improve the quality, productivity and performance of your work.
Provide support in a DevOps environment to monitor tokens, jobs and overall system performance. Qualifications
Bachelor's degree in computer science, engineering, mathematics, or a related technical discipline
Understand concepts of big data engineering, developing and maintaining ETL and ELT pipelines for data warehousing, on-premise and cloud data lake environments
Demonstrated production programming proficiency in at least one modern JVM language such as Java, Scala or Kotlin, as well as an interpreted declarative programming language such as Python
Entry level experience with AWS platform services, including AWS S3 & EC2, Data Migration Services (DMS), RDS, EMR, RedShift, Lambda, DynamoDB, CloudWatch, CloudTrail
Hands-on technical familiarity with Apache Spark architecture, S3, parquet and Delta Lake architecture, technologies and tools
Basic proficiency with both traditional relational and polyglot persistence technologies
Familiarity with agile software development & continuous integration + continuous deployment methodologies along with supporting tools such as Git (Gitlab), Jira, Terraform, New Relic
Familiarity with notebook environments including JupyterHub
Able to Communicate well with users, other technical team to collect requirements, describe data modeling decisions.

The employer’s company overview, benefits, equal-opportunity statement and generic skills tables are left out here; they are on the original posting.

Text from Corning's official job posting, formatted by HireSetu. The employer's own page is the final word on details.

Data science sits downstream

The title leads with AI, and the actual work is the layer underneath it. You pull data out of operational and process stores, get it landed, profile it, clean it, enrich it, and keep all of that running. Plenty of code, a fair amount of test and alerting code, and a lot of talking to the teams that own each source. If you pictured training models, this role produces the datasets those models use.

Reading the experience bar

One line says entry level, and it applies to the AWS services only. Production programming proficiency in Java, Scala or Kotlin, plus Python, is a working engineer's bar. The Spark, Parquet and Delta Lake familiarity is hands-on too. Two to four years of backend or data work, where a pipeline you owned fed other people, is the profile that fits this. Freshers will find the first screen hard.

Resume order

Put the two languages on line one, with something you shipped in each, then the pipeline you can describe end to end: source, landing, validation, scheduling, deployment. Name the AWS services from the listing that you have genuinely used instead of pasting the whole list. Validation and profiling deserve their own bullet, since a whole responsibility hangs on them. Gitlab, Jira, Terraform and New Relic are supporting tools here, so mention only what you have touched.

Ownership and the support rota

A chunk of this job is getting other teams to accept that the data you landed is correct, so ask which sources you would pick up first and who signs off on correctness. The DevOps monitoring duty on jobs and system performance is worth a direct question about rotation and hours, particularly around releases. This is an onsite Pune role, so settle what the release weeks look like in person.

ETL, ELT and the landing zone

ETL and ELT describe two orders for the same pipeline. Extract pulls records from process and operational stores; transform cleans, joins and reshapes them; load writes the result into a warehouse or data lake. ETL transforms before loading. ELT lands raw data first and transforms it inside the lake or warehouse, which suits the on-prem and cloud data lakes named in the job. AWS DMS handles movement from operational databases into a landing target, S3 holds landed objects, and RedShift serves as a warehouse for structured queries. Inbound data arrives from stores across the company, and validation, profiling and version control sit on those flows to keep them maintainable.

Parquet and Delta Lake in Spark

Parquet stores columns together, so a query that needs three fields reads only those three columns. Delta Lake puts a transaction log over Parquet files, giving Spark jobs table versions, schema enforcement and a record of added or removed files. An earlier version of a Delta table can be read after a bad write. Spark runs the transformation work across executors, with a driver coordinating stages and tasks. AWS EMR supplies managed Spark clusters for that processing. Continuous integration and continuous deployment checks run the same code paths before a change reaches production.

Profiling and validation as code

Profiling and validation are executable checks on incoming data. Profiling summarises a batch: null rates, distinct values, ranges and string patterns, which exposes a source that has changed shape. Validation applies agreed rules, such as required fields and accepted value sets, and fails the run when a rule breaks. Profiling output is what the rules get written against, and both run inside the ingestion job.

Cleansing code standardises and deduplicates records; enrichment adds derived fields or joins reference data. Git records every change to the pipeline code with a diff and a message, and Gitlab is the hosted system for it. Terraform describes infrastructure in configuration files so an environment can be rebuilt. New Relic reports on running jobs and system performance.

Prepared from Corning's job posting and checked against it before publishing. See our Editorial Policy.

How to apply

The Apply button opens this job on Corning's careers site (corningjobs.corning.com).

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