Principal Data Platform Architect - Hybrid (Remote 2 days)

1010 Analog Devices Inc.; Wilmington (MA); Hybrid; USD 217,000 - 251,000; Full time

Benefits offered by this job

Partial Telecommute

Job summary

Analog Devices, Inc. is seeking a Principal Engineer, Data Engineering to design and own end-to-end data architecture supporting AI and analytics. You will champion data modeling, ingestion, storage, and transformation for large-scale processing and mentor data engineers through coding standards and reviews.

You will collaborate with AI/ML and software teams to enable rapid experimentation and deployment on large datasets, driving data governance and observability across domains.

Qualifications

• Master’s or Bachelor’s degree in IT/CS/EE/CE (foreign equivalents considered).
• 5+ years defining, architecting and rolling out data products through their lifecycle.

Responsibilities

• Define and own end-to-end data architecture for scalable AI/analytics solutions.
• Set data modeling, ingestion, storage and transformation standards.
• Lead data governance, quality and observability across domains.
• Provide technical leadership and code reviews for data engineers.
• Collaborate with AI/ML and software teams to enable experimentation on large datasets.
• Mentor and lead complex data projects with cross-functional teams.

Skills

distributed systems

streaming systems

Spark

Kubernetes

Terraform

Kafka

Airflow

Hadoop

Informatica

Talend

SQL

Python

Looker

Tableau

GCP Dataflow

Azure Synapse

Data governance

Observability

Grafana

Datadog

Education

Master’s degree in IT/CS/EE/CE

Bachelor’s degree in IT/CS/EE/CE

Tools

Spark

Kubernetes

Terraform

Kafka

Pub/Sub

Airflow

Informatica

Talend

SQL

Python

Oracle

SQL Server

Vertica

Looker

Tableau

BigQuery

Dataflow

Redshift

Glue

Grafana

Datadog

Job description

Analog Devices, Inc. is seeking a Principal Engineer, Data Engineering to design and own end-to-end data architecture supporting AI and analytics. You will champion data modeling, ingestion, storage, and transformation for large-scale processing and mentor data engineers through coding standards and reviews.

You will collaborate with AI/ML and software teams to enable rapid experimentation and deployment on large datasets, driving data governance and observability across domains.