Since 2018, the head of risk has built his team to include XXXXX and an analyst. We helped hire the XXXX. The next iteration could involve you.
As you know, the risk models are dependent on perfect data. That's the goal of this position - to bring data perfection from 90% to 100%
Because of the importance of this position, you will be part of the risk department and therefore part of a business function that reports into the CEO and not part of a support function such as IT.
You'll learn from the best and bolt on business rule knowledge to your data engineering expertise.
We are looking for a Data Engineer to design, build, and maintain scalable data pipelines and infrastructure. The ideal candidate will work closely with data scientists, analysts, and software engineers to ensure high-quality, accessible, and reliable data across the organization.
In practical terms, this role ensures data flows from source to destination cleanly, quickly, and securely so analytics, data science, and applications can operate without bottlenecks.
This role sits at the center of the organization’s data ecosystem. The Data Engineer builds and operates systems that ingest data from multiple sources, transform it into trusted datasets, and make it available through data warehouses, data lakes, and curated models.
The position balances architecture, development, and operations, with a strong emphasis on reliability, performance, governance, and security.
You will own core data pipelines and contribute to the broader data platform roadmap. You will partner with analytics, product, and engineering teams to understand requirements, define datasets, improve accessibility, and ensure the platform scales as the organization grows.
Design, build, and maintain ETL/ELT pipelines for structured and unstructured data
Develop and optimize data warehouses and data lakes
Ensure data quality, reliability, and performance
Integrate data from multiple sources (APIs, databases, logs, third-party tools)
Collaborate with analytics and product teams to support reporting and modeling needs
Monitor pipelines, troubleshoot failures, and improve system efficiency
Implement data governance, security, and compliance best practices
Document data architecture, data models, and workflows
You are a pragmatic builder who values correctness and reliability. You can translate business questions into robust datasets, choose appropriate patterns for batch and streaming workloads, and deliver maintainable solutions with strong operational discipline.
Strong programming skills in Python and SQL; Scala or Java experience is a plus
Experience with modern data warehouses (Snowflake, BigQuery, Redshift, Azure Synapse)
Familiarity with ETL and orchestration tools (Airflow, dbt, Fivetran, Stitch, etc.)
Experience with cloud platforms (AWS, GCP, or Azure)
Solid understanding of database design, data modeling, and performance tuning
Knowledge of streaming technologies (Kafka, Kinesis, Pub/Sub) is a plus
Strong problem-solving and communication skills
Experience with big data technologies (Spark, Hadoop)
Understanding of DevOps and CI/CD practices for data systems
Knowledge of data privacy and compliance frameworks (GDPR, HIPAA, etc.)
Experience supporting machine-learning pipelines
Data pipelines run reliably with minimal downtime
Data is trusted, well-documented, and easy to access
Analytics and data science teams move quickly without data bottlenecks