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Self Financial is a venture-backed fintech company on a mission to increase economic inclusion and financial resilience by empowering people to build credit and savings. The company serves 100+ million Americans with no or low credit.
As Data Engineering Manager on the Data Platform team, you will lead a small, high-leverage team responsible for infrastructure and pipelines powering credit decisioning, risk, analytics, and financial reporting. This is a hands-on technical manager role—you'll stay deeply involved in architecture reviews, design decisions, and unblocking hard technical problems while managing and growing a team of data engineers.
The data platform underpins everything from real-time risk signals to executive reporting and is undergoing active modernization, including a multi-quarter migration toward a lakehouse architecture. You'll own the health, reliability, and roadmap of core data pipelines and platform services.
Responsibilities include: managing and growing a team of data engineers (hiring, mentoring, performance management); owning architecture and design decisions for the data warehouse, transformation pipelines (dbt), and orchestration layer; actively participating in technical design reviews and code reviews; driving the modernization roadmap including migration to Iceberg/Spark and medallion-tier architecture; partnering with Analytics, Risk, and Infrastructure teams to translate business needs into platform capabilities; establishing data platform governance (model maturity standards, access controls, data quality checks); and evaluating new tooling and infrastructure as the platform scales.
You should be a practitioner in modern data infrastructure who has designed, built, or operated warehouse/lakehouse systems in production. You can read and critique dbt models, pipeline DAGs, and schema designs. You're comfortable being a technical contributor in platform design conversations while managing people without losing your technical edge. You care deeply about data quality, governance, and reliability alongside throughput.
Required: 4+ years in data engineering management or tech-lead-manager roles directly managing engineers; 7+ years hands-on data engineering experience building and operating pipelines, warehouses, or lakehouse platforms in production; experience with modern data stack (cloud data warehouse like Redshift/Snowflake/BigQuery, transformation framework like dbt, orchestration tool like Airflow/Dagster). Strongly preferred: working knowledge of streaming/event pipelines (Kafka) and lakehouse table formats (Iceberg, Delta Lake); track record of architecture decisions that held up in production.
