Data Engineering Manager

As a Data Engineering Manager on our Data Platform team, you will lead a small, high-leverage team responsible for the infrastructure and pipelines that power credit decisioning, risk, analytics, and financial reporting across Self Financial. You'll stay hands-on with the architecture reviewing designs, unblocking hard technical problems, and setting direction on our data warehouse, transformation layer, and orchestration while managing and growing a team of data engineers. This is a working technical manager role, not a generalist engineering management role: we need someone who has actually built and operated modern data platforms, not just managed teams that did.

Teams that make a difference; individuals that matter

At Self Financial, we put hard problems in the hands of people who've solved them before. Our data platform underpins everything from real-time risk signals to executive reporting, and it's undergoing active modernization including a multi-quarter migration toward a lakehouse architecture. We want a leader who can go deep in design reviews, not just in headcount planning. This role presents an incredible opportunity for you to have a before and after transformation of an enterprise data warehouse in your career.

As Data Engineering Manager, you will own the health, reliability, and roadmap of our core data pipelines and platform services, while directly managing a team of data engineers. You'll split your time between hands-on technical leadership architecture reviews, pairing with engineers on hard problems, evaluating tooling and infrastructure choices and traditional people management: hiring, mentoring, performance, and process. You'll partner closely with Analytics, Risk, and Platform/Infrastructure teams to make sure the data platform serves real business decisions, not just dashboards.

• You've been a practitioner in modern data infrastructure you've designed, built, or operated warehouse/lakehouse systems, not just directed others who did
• You can read and critique a dbt model, a pipeline DAG, or a schema design and have an informed opinion
• You're comfortable being a contributor in a data platform design conversation
• You want to manage people, but you don't want to lose your technical edge doing it
• You care about data quality, governance, and reliability as much as throughput.
• You like the idea of helping people build (or rebuild) their financial future through the data that powers it

• →Managing and growing a team of data engineers, including hiring, mentoring, and performance management
• →Owning architecture and design decisions for our data warehouse, transformation pipelines (dbt), and orchestration layer
• →Actively participating in technical design reviews and code/PR reviews
• →Driving our modernization roadmap, including our migration to Iceberg/Spark and to build a warehouse in medallion-tier architecture.
• →Partnering with Analytics, Risk, and Infrastructure teams to translate business and reporting needs into platform capabilities
• →Establishing and enforcing data platform governance: model maturity standards, access controls, data quality checks
• →Evaluating and introducing new tooling, frameworks, and infrastructure as the platform scales

• 4+ years in a data engineering management or tech-lead-manager role, directly managing engineers
• 7+ years of hands-on data engineering experience - building and operating pipelines, warehouses, or lakehouse platforms in production
• Specific experience with a modern data stack: a cloud data warehouse (e.g., Redshift, Snowflake, BigQuery), a transformation framework (e.g., dbt), and an orchestration tool (e.g., Airflow, Dagster)
• Working knowledge of streaming/event pipelines (e.g., Kafka) and lakehouse table formats (e.g., Iceberg, Delta Lake) is strongly preferred
• A track record of making architecture decisions that held up in production, not just process improvements
• Excellent written and verbal communication skills, including with non-technical stakeholders
• Experience with agile engineering methodologies