Job description

About the Role 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. Responsibilities Data Product & Governance - 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. Hands-on Engineering & Delivery - 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. Leadership & Team-building - 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. Qualifications - 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. - 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. Our Stack Snowflake, dbt, Sigma, MWAA / Airflow, AWS (S3, Lambda), GitHub Enterprise, Terraform; Bloomberg and FactSet; Okta for identity; and Claude Enterprise, Claude Code, Kiro, and AWS Bedrock for AI-assisted engineering. Compensation Range: - $220,000-$230,000 plus annual bonus Global X does not accept unsolicited resumes or candidate profiles from recruitment agencies, search firms, or third-party recruiters. Compensation & Benefits Global X offers a competitive total compensation package, including base salary, annual performance bonus, and a comprehensive benefits program: - Competitive base salary with annual performance-based bonus - Comprehensive medical, dental, and vision insurance - 401(k) retirement plan with company matching - Generous paid time off and company holidays - Professional development and continuing education opportunities - Hybrid work flexibility - Life and disability insurance - Wellness programs and employee assistance program (EAP) About Global X Global X was founded in 2008. For more than fifteen years, our mission has been empowering investors with unexplored and intelligent solutions. Our product lineup features a wide range of ETF strategies and more than $94 billion in assets under management as of June 30, 2026. While we are distinguished for our thematic, income, and international ETFs, we also offer core and other funds to suit a wide range of investment objectives. Explore our ETFs, research and insights, and more at www.globalxetfs.com globalxetfs.com. Global X is a member of Mirae Asset Financial Group (Mirae Asset), a global leader in financial services, with $1 trillion in assets under management worldwide as of May 31, 2026. Mirae Asset has an extensive global ETF platform ranging across the U.S., Australia, Brazil, Canada, Colombia, Europe, Hong Kong, India, Japan, Korea,

Requirements

Must-haves

Data Product & Governance
Stand up the firm's first real intake, prioritization, and roadmap for data work — replacing ad-hoc triage with a visible, defensible backlog.
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.
Hands-on Engineering & Delivery
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.
Leadership & Team-building
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.
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.
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.
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.
Snowflake, dbt, Sigma, MWAA / Airflow, AWS (S3, Lambda), GitHub Enterprise, Terraform; Bloomberg and FactSet; Okta for identity; and Claude Enterprise, Claude Code, Kiro, and AWS Bedrock for AI-assisted engineering.
Global X does not accept unsolicited resumes or candidate profiles from recruitment agencies, search firms, or third-party recruiters.

Nice-to-haves

$220,000-$230,000 plus annual bonus

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Benefits

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Dental

Vision

401(K)

Paid Time Off

Wellness

Professional Development