Data Engineer II: ELT Pipelines & Observability

Northwestern Mutual; Milwaukee (WI); On-site; USD 89,000 - 134,000; Full time

Benefits offered by this job

Flexible work schedules

Concierge service

Comprehensive benefits

Employee resource groups

Job summary

Northwestern Mutual is seeking a data engineer to design, build, and maintain scalable ELT pipelines using Python, PySpark, and SQL, delivering reliable data products for analytics and reporting. You will own production reliability, monitor SLAs, and drive improvements across the data stack.

You'll collaborate with stakeholders to translate business needs into robust data solutions, work with Databricks and Snowflake, and participate in code reviews and mentoring.

Qualifications

• 2-5 years of professional experience in data engineering or software engineering with production systems.
• Strong Python programming skills and distributed processing experience (PySpark).
• Solid SQL skills and experience with cloud data platforms (Snowflake/Databricks).
• Experience with orchestration tools (Airflow/Control-M) and CI/CD pipelines.

Responsibilities

• Design, implement, and maintain ELT pipelines and transformations using Python/PySpark/SQL.
• Own production reliability: monitor SLAs, respond to incidents, reduce MTTR.
• Implement and maintain CI/CD pipelines to automate ELT changes.
• Orchestrate workflows with Airflow or Control-M; manage retries/backfills/schema changes.
• Build and maintain streaming integrations (Kafka/Kinesis) for near real-time use cases.
• Ensure data quality and observability with checks and monitoring.

Skills

Python

PySpark

SQL

Airflow

CI/CD

Git

Databricks

Kafka

Kinesis

Tools

Databricks

Snowflake

Kubernetes

Terraform

Docker

Control-M

Job description

Northwestern Mutual is seeking a data engineer to design, build, and maintain scalable ELT pipelines using Python, PySpark, and SQL, delivering reliable data products for analytics and reporting. You will own production reliability, monitor SLAs, and drive improvements across the data stack.

You'll collaborate with stakeholders to translate business needs into robust data solutions, work with Databricks and Snowflake, and participate in code reviews and mentoring.