Senior ML Data Platform Engineer

Cognizant; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Cognizant is seeking an experienced software engineer to build an ML data infrastructure platform. You will design scalable data processing pipelines, feature stores, and dataset versioning to power model development and evaluation.

In this role you will collaborate with data scientists, data engineers, and security teams to ensure data quality, reproducibility, and compliant data access patterns across our data lakes and warehouses on GCP.

Qualifications

• 7+ years of software engineering experience, with 3+ years in data infrastructure.
• Strong expertise in GCP data and ML infrastructure (BigQuery, Dataflow, Cloud Storage, Cloud Composer, Dataproc).
• Proficiency in Python and SQL.
• Experience with data quality and testing frameworks.
• Experience with data governance, security, and IAM best practices.

Responsibilities

• Design and implement scalable data processing pipelines for ML training and validation
• Build and maintain feature stores with support for both batch and real-time features
• Develop data quality monitoring, validation, and testing frameworks
• Create systems for dataset versioning, lineage tracking, and reproducibility
• Implement automated data documentation and discovery tools
• Design efficient data storage and access patterns for ML workloads
• Partner with data scientists to optimize data preparation workflows

Skills

Python

SQL

Data pipelines

Data governance

Cloud data platforms

Spark / Beam / Flink

Machine learning data infrastructure

Tools

BigQuery

Dataflow

Cloud Storage

Cloud Composer

Dataproc

Spark

Beam

Flink

Feast

Tecton

Airflow

Dagster

Kafka

Kinesis

Job description

Cognizant is seeking an experienced software engineer to build an ML data infrastructure platform. You will design scalable data processing pipelines, feature stores, and dataset versioning to power model development and evaluation.

In this role you will collaborate with data scientists, data engineers, and security teams to ensure data quality, reproducibility, and compliant data access patterns across our data lakes and warehouses on GCP.