Lead and develop three sub-teams: Platform Engineering & ETL, Analytics Engineering, and Data Science & Analytics. Manage leads, set priorities, and ensure delivery.
Own the Data Lakehouse architecture: Trino, Iceberg/GCS, Airflow, Airbyte, Redpanda CDC, dbt. Make build-vs-buy decisions on tooling.
Drive partner invoicing accuracy and evolution: ensure invoicing logic is versioned, reproducible, and scales with new pricing mechanisms and product launches.
Deliver embedded analytics: expose warehouse data to partners via BrokerDash, SSR pipelines, and API-based reporting. Own row-level security and entitlements.
Support product launches with data change management: coordinate data impact analysis for new products (fixed income, global stocks, perps, 24/5 trading) across downstream datasets, dashboards, and reverse ETL.
Accelerate self-service: move the organization toward self-serve analytics via semantic layers, data catalogues, and conversational BI so the data team can shift from ad-hoc queries to strategic projects.
Guide AI/ML enablement: oversee enterprise AI search, agent-based workflow automation, and LLM-powered analytics. Help balance vendor solutions with in-house development.
Collaborate with Finance, Sales, Product, Compliance, and Customer Success to translate business needs into data products.
Manage infrastructure costs: keep data + cloud cost ratio under target as AUC grows.
Operate production systems: own on-call processes, incident response, and SLOs for data freshness, accuracy, and availability.
Requirements
8+ years in data engineering or analytics, including 3+ years managing data teams (leads + ICs).
Deep experience with modern data stack: dbt, Trino/Presto or equivalent query engines, Apache Iceberg or similar table formats, cloud object storage.
Hands-on experience with ETL/ELT patterns at scale: CDC (Debezium/Kafka), batch (Airflow/dbt), streaming, and reverse ETL.
Track record of building self-service analytics capabilities for non-technical stakeholders.
Experience with financial data: trading, invoicing, revenue attribution, or regulatory reporting in fintech or financial services.
Proficiency in Python and SQL. Comfortable reading code, reviewing PRs, and making architecture decisions.
Experience managing distributed/remote teams across multiple time zones.
Strong stakeholder management: you can translate between executive priorities and engineering execution.
Experience with GCP (GKE, GCS, BigQuery migration), Kubernetes, Helm, Terraform.
Tech Stack
Airflow
Apache
BigQuery
Cloud
ETL
Google Cloud Platform
Kafka
Kubernetes
Python
SQL
Terraform
Benefits
Competitive Salary & Stock Options
Health Benefits
New Hire Home-Office Setup: One-time USD $500
Monthly Stipend: USD $150 per month via a Brex Card