Staff Data Engineer — Hybrid (Remote 2 days)

Analog Devices, Inc.; Wilmington (MA); Hybrid; USD 179,000 - 210,000; Full time

Benefits offered by this job

Partial telecommute up to 2 days/week

Medical, vision, dental coverage

401k and paid time off

Job summary

Analog Devices, Inc. is seeking a Staff Engineer, Data Engineering to architect and build scalable big data pipelines, ETL, and real-time analytics for battery data.

You will translate business needs into pipelines, coordinate deliverables with internal and external stakeholders, and ensure data quality and governance across the stack. You will deploy ML workflows in production, collaborate with algorithm and device engineers, and continuously evaluate data infrastructure, tools, and processes.

Qualifications

• Master's degree in CS/CE/SE or related field.
• 5+ years of data engineering or staff engineer-level experience.
• Proficiency in Python or Spark for building scalable data pipelines.
• Experience with cloud-based data infrastructure and ML workflow integration.

Responsibilities

• Architect, design, and maintain scalable big data pipelines and ETL processes.
• Translate business requirements into functional data pipeline specs and coordinate deliverables.
• Identify and implement scalable data storage and retrieval solutions.
• Ensure data quality, integrity, and accuracy through validation and cleansing.
• Deploy and support ML workflows and AI/ML models in production with engineers.

Skills

Python

Spark

SQL

Data modeling

Time-series data

Cloud data infra

Education

Master's degree in Computer Science / Computer Engineering / Software Engineering / Data Engineering

Tools

DBT

Snowflake

Redshift

Iceberg/Delta Lake

Kafka

Airflow

Prefect

SageMaker

Docker

Kubernetes

GitHub

Job description

Analog Devices, Inc. is seeking a Staff Engineer, Data Engineering to architect and build scalable big data pipelines, ETL, and real-time analytics for battery data.

You will translate business needs into pipelines, coordinate deliverables with internal and external stakeholders, and ensure data quality and governance across the stack. You will deploy ML workflows in production, collaborate with algorithm and device engineers, and continuously evaluate data infrastructure, tools, and processes.