VP, Data Products & Platform Lead

Global X ETFs•New York, NY

7h•Hybrid

About The Position

We are hiring the founding lead for data products and platform: a senior, hands-on builder who will own how the firm defines, governs, and ships its core investment data, and who will grow a small team around them. You will sit at the center of a federated model — every desk, from portfolio management and trading to portfolio administration, finance, research, and compliance, already builds with our data. Your job is to turn that energy into trusted, shared, and governed products rather than fragmented private workarounds. This is a player-coach role: you will set product direction, write production code, and hire and lead as the function grows.

Requirements

8+ years in data, with a genuine blend of hands-on data engineering and data-product ownership — you have both shipped pipelines and models and owned definitions and roadmaps.
Deep buy-side / asset-management data domain: security and fund master, holdings, transactions, corporate actions, benchmarks and index constituents, identifiers, and NAV — plus the vendor feeds behind them (Bloomberg, FactSet, fund-accountant data).
Expert SQL and Python; production experience with Snowflake and dbt (or close equivalents).
Demonstrated ownership of a canonical data model or data standards across multiple consuming teams.
A track record of building and leading a small data team — or clear, evidenced readiness to step into a founding-lead role.
Strong written and verbal communication; able to set definitions and defend prioritization with executives and compliance.

Nice To Haves

ETF / 1940-Act fund experience; familiarity with SEC recordkeeping (Rule 204-2), Regulation S-P, 15(c) board reporting, and the Tailored Shareholder Reports regime.
Modern-stack depth: data catalog (e.g., OpenMetadata, Atlan), orchestration (Airflow / MWAA), managed ingestion, BI (Sigma), Snowflake Cortex and semantic views.
Hands-on with AI-assisted engineering (e.g., Claude Code, Codex, Kiro) and with building self-serve, semantic, or agent layers over governed data.
AWS and Terraform / infrastructure-as-code; experience standing up data governance in a regulated environment.

Responsibilities

Stand up the firm's first real intake, prioritization, and roadmap for data work — replacing ad-hoc triage with a visible, defensible backlog.
Learn how each function uses the same data, then reconcile divergent definitions into a single set of canonical, documented entities — fund and security master, holdings, classifications, corporate actions, index data — that the firm trusts and reuses.
Own the data-product lifecycle: turn recurring needs into governed shared products with clear ownership, SLAs, and a catalog that makes the canonical layer discoverable.
Drive adoption, measured by the number of teams using the shared layer instead of querying raw vendor tables.
Build and ship. Write production dbt models in Python and SQL, and design the serving and semantic layers. You are expected to be in the codebase, not only the roadmap.
Harden the platform: alerting on data freshness and pipeline runs, workload isolation, least-privilege service accounts, and managed, monitored ingestion.
Partner on the market data and internal reporting build and the Bloomberg / FactSet data consolidation already underway; make the canonical layer the obvious, safe starting point for AI-assisted building across the firm.
Build and lead a small, senior data team; mentor existing engineers and the embedded analysts across the business.
Establish the operating model — shared repository, review and approval workflow, standards — with the central team as reviewer and enabler, not gatekeeper.
Be the firm's senior voice on data, translating fluently between business stakeholders, compliance, and engineering.