Lead Data Platform Engineer – Investment Data Lakehouse

Castleton Tower; New York (NY); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Castleton Tower is seeking a senior data engineering lead to build a modern investment data platform for a prominent asset allocator client. You will design the warehouse/lakehouse, craft data models, and lead a small team while partnering with investment, operations, and reporting stakeholders.

You will write production-grade Python/SQL/dbt pipelines, establish CI/CD, observability, and data quality controls, and set engineering standards across the team to deliver durable data products with

Qualifications

• 8+ years of hands-on experience in data engineering, analytics engineering, or software engineering focused on data platforms.
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Responsibilities

• Architect and build core investment data domains such as security and entity reference data, positions and transactions, performance, risk, and private-markets fund data.
• Design data models that are correct, reconcilable, and point-in-time aware, with tests and controls that catch problems before users do.
• Own reliability, performance, and cost of the platform, including monitoring, alerting, and incident follow-through.
• Review others’ work closely, send it back when it isn’t right, and make the next version better.
• Evaluate tooling and vendors across Snowflake, dbt, Dagster/Airflow, and Azure, and make pragmatic build-versus-buy calls.

Skills

8+ years data eng

Technical leadership

SQL & Python

Data modeling

Snowflake/Databricks

dbt

Dagster/Airflow

CI/CD for data

Code reviews

Tools

Snowflake

Databricks

Dagster

Airflow

Azure

dbt

Job description

Castleton Tower is seeking a senior data engineering lead to build a modern investment data platform for a prominent asset allocator client. You will design the warehouse/lakehouse, craft data models, and lead a small team while partnering with investment, operations, and reporting stakeholders.

You will write production-grade Python/SQL/dbt pipelines, establish CI/CD, observability, and data quality controls, and set engineering standards across the team to deliver durable data products with