Data Engineer

Northwestern Mutual Group; Milwaukee (WI); Hybrid; USD 98,000 - 160,000; Full time

Benefits offered by this job

Telecommuting 2 days/week

Job summary

Northwestern Mutual Life Insurance Company in Milwaukee, WI is seeking a Data Engineer to design scalable data pipelines and models using Big Data technologies. You will ingest, transform, and load data from multiple sources to power analytics, reporting, KPI tracking, and ML workflows.

You will build analytical datasets and dashboards with SQL and Python, develop ML features, monitor models in production, and contribute to data governance and data quality initiatives.

Qualifications

• 2 years of data engineering experience with Python and SQL.
• Big Data technologies including JVM (Java, Scala), Apache Spark, PySpark.
• Designing data models, schemas, and storage architectures for analytical use.
• Agile/Scrum software development and DevOps practices in a team.
• Data Integration: ELT/ETL, replication, event streaming, virtualization.

Responsibilities

• Design and build scalable data pipelines and data models.
• Ingest, transform, and load data from multiple source systems.
• Develop analytics datasets, KPI metrics, and dashboards.
• Build ML features and monitor model performance in production.
• Establish data governance, quality, and metadata standards.

Skills

Python

SQL

Apache Spark

PySpark

ETL/ELT

Agile/Scrum

DevOps

Education

Bachelor’s degree in Computer Science/Engineering/Info Tech/Data Science

Tools

Java

Scala

Job description

Job Description: The Data Engineer at Northwestern Mutual Life Insurance Company in Milwaukee, WI will design and build scalable data pipelines and data models using Big Data technologies. Ingest, transform, and load structured and unstructured data from multiple source systems. Architect data infrastructure across the Financial Planning and Illustrations domains to power analytics, reporting, KPI tracking, predictive modeling, and machine learning workflows. Build analytical datasets, KPI metrics, and interactive dashboards using SQL, Python, and visualization tools. Create trend analyses, performance reports, and business intelligence solutions for Financial Representatives, business stakeholders, and leadership. Develop data aggregation and reporting frameworks that translate raw financial planning data into actionable insights for decision-making. Design and build predictive models and machine learning (ML) solutions for client retention analysis, churn forecasting, and financial planning optimization. Develop feature engineering pipelines and curated training datasets. Deploy ML models into production environments and monitor model performance, accuracy, and drift. Architect and develop AI-powered solutions to automate data pipeline monitoring, data quality detection, and data integration issue resolution. Build intelligent anomaly detection systems that proactively identify data inconsistencies, schema drift, and pipeline failures before they impact downstream analytics and business reporting. Build and deploy end-to-end data products, ETL/ELT jobs, and data processing workflows using SQL, Python, and Big Data tools. Design data storage solutions, schema architectures, and optimized query structures within the enterprise data platform. Implement continuous deployment and integration practices across all data engineering solutions. Design and implement data quality frameworks, automated validation rules, and reconciliation processes. Build data auditing systems to detect root causes of data issues across the enterprise data ecosystem. Develop data governance, cataloging, and metadata management standards across all data assets, with emphasis on PII compliance, access controls, and regulatory adherence. Build monitoring and alerting systems for production data pipelines and analytics solutions. Develop automated health checks and performance optimization routines. Mentor junior developers and conduct code reviews to uphold engineering standards and code quality. Lead cross-functional collaboration with platform, MLOps, and data management teams to architect scalable, production-grade data and AI solutions. Drive Agile delivery through sprint planning, backlog grooming, story writing, sizing, and demo sessions. Telecommuting permitted 2 days per week. Salary $97,552- $159,770 per year.

Minimum Requirements: Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, Data Science, or a related field and 2 years of experience as data engineer or related occupation. Experience must include 2 years of experience with each of the following: (1) developing data pipelines, ETL/ELT workflows, and data processing solutions using Python and SQL; (2) Big Data technologies including JVM (Java, Scala), Apache Spark, PySpark for large-scale data processing and analytics; (3) designing and implementing data models, database schemas, and data storage architectures for analytical and operational use; (4) Agile/Scrum software development methodologies and DevOps practices in a collaborative team environment; (5) Data Integration Patterns and Tooling including ELT/ETL, Replication, Event Streaming, and Virtualization to support batch and real-time data needs; and (6) applying Software and Quality Standards in a professional development environment.

We believe in fairness and transparency. It’s why we share the salary range for most of our roles. However, final salaries are based on a number of factors, including the skills and experience of the candidate; the current market; location of the candidate; and other factors uncovered in the hiring process. If noted, this is standard pay structure for this position.

Northwestern Mutual is an equal opportunity employer that welcomes talented individuals of all backgrounds. We are committed to creating and maintaining an environment in which each employee can contribute creative ideas, seek challenges, assume leadership and continue to focus on meeting and exceeding business and personal objectives.