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West Bend, Wisconsin, United States
Onsite
About the team
Field domain data team build and manage data pipelines, create pipelines to collect data from multiple sources (databases, APIs, files), transform, and move data into storage systems (Data Mesh/data lakes / warehouses), Automate ETL (Extract, Transform, Load) workflows. Design scalable data architecture, systems that can handle large-scale data reliably and efficiently. Ensure data quality, governance & security, validate and clean incoming data, apply governance policies (who can access what), maintain compliance and security controls.
We work with other teams across organization, collaborate with Product/ business teams (to understand data needs), Data scientists (to support ML/AI use cases), DevOps / platform teams.
What You'll do
- Lead the design and delivery of end-to-end data platforms and pipelines (batch and streaming) using AWS services to support analytics, reporting, ML, and operational use cases.
- Define and own data architecture decisions, standards, and best practices. Produce and maintain Architecture Decision Records (ADRs), runbooks, and high-quality diagrams.
- Architect and implement data lake and/or data warehouse solutions (ingestion, storage, transformation, serving) using S3, Glue, Redshift, Athena, and related services.
- Build and optimize ETL/ELT workflows with Spark, Glue, Lambda, Step Functions, and orchestration frameworks (Airflow, Dagster).
- Design and operate streaming ingestion and processing using Kinesis, MSK, and Kafka ecosystems; collaborate on re-architecture efforts to reduce cost and improve resilience.
- Drive data modeling, schema design, partitioning, and query optimization for large data sets to meet performance and cost targets.
- Establish and operationalize data quality, validation, observability, lineage, and metadata practices (Glue Data Catalog, Data Hub/Amundsen or equivalent).
- Implement robust security, governance, and compliance controls (IAM, Lake Formation, KMS, VPC, data masking/security scanning) and collaborate with security and privacy teams.
- Champion cost optimization across the data stack (serverless architectures, right-sizing, Graviton/spot/commitment strategies) and provide continuous cost governance.
- Own production reliability: monitoring, SLIs/SLOs, incident response, postmortems, and continuous improvements.
- Lead and mentor engineers, run design reviews, and promote cross-team collaboration with product, analytics, ML, and platform teams.
- Influence roadmap, capacity planning, and hiring for the data engineering function; represent the team in senior technical forums.
What You'll bring to the role
- 7+ years of hands-on experience in data engineering, architecting and operating data platforms at scale.
- Demonstrated leadership on large, complex data projects and strong ability to influence technical and product stakeholders.
- Extensive hands-on experience with AWS data services: S3, Glue, Redshift (Spectrum/RA3), Athena, EMR, Lambda, Kinesis/MSK, DMS.
- Strong programming skills in Python and/or Scala (or Java); proven track record writing production-quality, testable code.
- Deep expertise in SQL with experience designing and optimizing queries for high-volume data.
- Experience with distributed processing frameworks (Spark) and modern ETL/ELT patterns.
- Proficiency with orchestration and workflow tools (Airflow, Step Functions, Dagster) and CI/CD for data pipelines.
- Strong skills in infrastructure-as-code (Terraform, CloudFormation, or CDK) and GitOps practices.
- Experience implementing data security, governance, and access controls at scale.
- Expertise in observability, monitoring, and incident management (CloudWatch, Prometheus, Grafana, structured logging, tracing).
- Excellent communication skills and proven ability to mentor and lead engineers.
Preferred Qualifications
- AWS certifications (e.g., AWS Certified Data Analytics – Specialty, AWS Certified Solutions Architect) or equivalent.
- Experience with data lakehouse technologies (Delta Lake, Iceberg, Databricks Unity Catalog), and large-scale performance troubleshooting.
- Familiarity with metadata and lineage tooling (Amundsen, DataHub, OpenLineage).
- Prior experience with cost-optimization programs (Graviton, serverless, spot instances) and demonstrable results.
- Contributions to open-source projects, publications, or conference presentations on data engineering topics.
- Experience working in regulated industries (finance, healthcare) and implementing compliance controls.
-AI usage for building Data Pipelines.
Skills You'll have
- Strategic thinker with strong problem-solving and architectural judgment.
- Ability to manage multiple complex priorities and deliver high-quality work under deadlines.
- Strong mentoring and team leadership skills; fosters a culture of knowledge sharing and continuous improvement.
- Customer-focused: translates business needs into pragmatic, maintainable technical solutions.
- Strong attention to detail, excellent documentation habits, and commitment to operational excellence.
Compensation Range:
Pay Range - Start:
$118,960.00Pay Range - End:
$178,440.00Geographic Specific Pay Structure:
Structure 110:
Structure 115:
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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.