AVP, Data Engineer

Vantage Risk•Chicago, IL

2d•Hybrid

About The Position

At Vantage, the AVP, Data Engineer ingests and transforms replicated source data into data products that are readily consumed by the Reporting team and other downstream users. The role calls for expertise in data modeling and data pipeline development within an analytical, column-oriented data warehouse. This is a hybrid opportunity for a candidate within commuting distance of a Vantage office, with New York or Chicago preferred. The role reports to the VP, Data Engineering & ETL Lead. The position combines hands-on engineering with technical leadership. The AVP translates requirements and data-mapping documents into technical designs, develops SQL and dbt transformations and Dagster pipelines, diagnoses data issues, and supports continuous improvement. The successful candidate will also mentor engineers and promote technical best practices across the team.

Requirements

Bachelor's degree in Computer Science or an equivalent combination of education and professional experience.
At least 8 years of data engineering experience designing, building, and supporting enterprise data platforms, ingestion frameworks, and transformation pipelines.
Strong expertise in SQL, Python, and dbt, including developing and optimizing large-scale ETL/ELT solutions.
Hands-on experience with Dagster, Airflow, or similar workflow orchestration tools.
Experience building and managing ingestion and replication pipelines from relational databases such as PostgreSQL and Oracle, NoSQL sources, and file-based feeds into Snowflake, Databricks, or comparable modern data platforms.
Deep understanding of data modeling concepts, including dimensional modeling, Medallion Architecture, Data Vault, and highly relational data structures.
Experience transforming complex business data into trusted, consumable datasets for reporting, analytics, and downstream business users.
Strong knowledge of data engineering best practices, including testing, code reviews, Git version control, and CI/CD processes.
Familiarity with distributed data processing technologies, Kubernetes, and modern cloud-based data ecosystems.
Demonstrated ability to mentor junior engineers, conduct code reviews, and drive technical best practices across a team.
Proven success operating in a player-coach capacity, balancing hands-on development with technical leadership and team guidance.
Ability to process and adapt to change in a rapid-growth company environment.
Curiosity, thrift, and resourcefulness to manage uncertainty while growing a specialty (re)insurance business.

Nice To Haves

Experience in the Property & Casualty Insurtech or Financial Services industry.

Responsibilities

Translate requirements and data-mapping documents into a technical design.
Develop SQL using dbt.
Develop pipelines using Dagster.
Identify the specific records produced as a result of SQL bugs.
Debug and troubleshoot issues found during testing or production.
Communicate project status, issues, and blockers with the team.
Identify and address opportunities for continuous improvement.
Mentor junior engineers, conduct code reviews, and drive technical best practices across a team.
Balance hands-on development with technical leadership and team guidance.