Director of Data Products

Job Description

About Zacks

Independent research and proprietary data since 1978

Zacks Investment Research is a leading provider of independent equity research, consensus earnings estimates, proprietary ratings such as the Zacks Rank and Style Scores, and financial datasets with decades of history. Our data and research are used by institutional investors, wealth management firms, quantitative researchers, fintech platforms and AI companies. Because Zacks owns its research and data end to end, we can license it in ways many providers cannot, including for AI applications and model training.

The Opportunity

Turn a deep data archive into documented, sellable products

Zacks has more data and research content than most of its competitors, and demand for it is growing quickly, especially from AI companies, hedge funds and enterprise platforms.

The Director, Data Products will own the Zacks institutional data portfolio from end to end. You will be the person who knows what every dataset contains, how it is constructed and how it is delivered. You will turn that knowledge into clear documentation, standard samples and reliable trials. You will work across Sales, Data Operations, Technology and Research, and you will join prospect and client calls as the go-to data expert and technical voice.

Key Responsibilities

What you will own

Product ownership

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• Become the expert on every Zacks dataset and how clients use it, from quantitative research and backtesting to AI applications and platform integrations.
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• Identify data that exists internally but is not yet productized, and package it for sale with clear coverage, history and delivery specifications.
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• Provide the technical inputs Sales needs to set packages and pricing, including coverage, history depth, update frequency, delivery options and AI training or MCP use cases.
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Documentation and methodology

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• Build and maintain the external data catalog, data dictionaries, file format guides and API documentation.
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• Write methodology documents that answer prospect questions without requiring them to test the data themselves, starting with consensus construction and the handling of stock splits, corporate actions and restatements.
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• Keep product listings on third-party data marketplaces accurate, including data sources, collection methodology, data lag, history and trial terms.
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• Capture institutional knowledge from long-tenured team members so that no product depends on a single person.
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Samples, trials and onboarding

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• Build and maintain a library of standard sample files for each major dataset, including representative edge cases (splits, corporate actions, delisted companies, non-U.S. listings) so prospects can see how complex situations are handled.
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• Apply a tiered approach: standard samples for most prospects, with deeper custom evaluation reserved for larger opportunities.
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• Manage trial access across FTP/SFTP and API, from provisioning and permissions through expiration.
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• Lead technical onboarding for new clients and serve as the primary technical contact on sales calls with quants, data engineers and AI teams.
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Data quality

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• Run a data quality program with defined internal and external error-rate targets.
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• Ensure every sample and delivery is reviewed before it goes to a client or prospect.
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• Audit APIs against the underlying flat files, and check consistency between Zacks products that cover the same data.
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• Work with Technology to diagnose and prioritize fixes for systemic issues, such as split adjustment in historical consensus data.
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Delivery and modernization

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• Coordinate delivery through FTP/SFTP flat files, REST APIs, cloud data platforms (such as Snowflake or client data lakes) and MCP.
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• Define when each delivery method fits, for example seeding history by flat file and keeping it current by API.
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• Partner with Technology on moving datasets from legacy production environments to a modern, scalable stack, and on tools that make samples and custom extracts fast to produce.
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Data governance

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• Maintain an accurate record of which clients receive which feeds, and make sure access matches active contracts.
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• Work with Legal and Finance to document third-party data sources, licensing rights and costs, and to confirm what can be redistributed.
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What Success Looks Like

A practical view of the first 90 days

Timeframe

What success looks like

First 30 days

Learn every core dataset, file and API. Shadow the data operations team on live sample, trial and onboarding requests. Build a complete inventory of what Zacks produces, how much history exists and how each product is delivered.

By 60 days

Publish standard sample files for the core datasets. Publish consensus methodology and corporate actions documentation. Take ownership of the trial provisioning process for both FTP and API.

By 90 days

Own sample and trial requests end to end with a consistent turnaround. Complete the accuracy review of all Zacks APIs. Recommend priorities for data quality, documentation and delivery modernization.

Ongoing

Shorter enterprise sales cycles, fewer data issues found by prospects, an accurate client entitlement list, and product knowledge that is documented rather than held by a few individuals.

Required Qualifications

What you bring

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• Deep, hands-on expertise in financial data, including how earnings estimates and consensus, analyst revisions and ratings, fundamentals, pricing and corporate actions are collected, calculated and used by investors. This is essential; strong data science skills alone are not enough.
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• 5+ years working with financial datasets in data science, quantitative research, data engineering or data product roles at a financial data vendor, exchange, research provider or buy-side firm.
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• Strong grounding in statistics and backtesting methods, including point-in-time data construction and avoiding survivorship and look-ahead bias.
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• Advanced Python (pandas or similar) for data analysis, quality checks, file reconciliation and automated sample generation.
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• Hands-on SQL across multiple database platforms (for example Oracle and SQL Server), with the ability to query, compare and extract data independently.
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• Comfort working directly with large flat files, APIs and FTP/SFTP delivery, including basic command-line tools for searching and inspecting raw files.
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• Excellent written communication, with a record of producing clear technical documentation.
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• Confident and polished with clients, including technical conversations with hedge funds, quantitative researchers, data engineers and AI companies.
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• Strong attention to detail and a habit of checking work before it reaches a client.
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• Able to work across teams and get results in an organization that is still building its processes.
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Preferred Qualifications

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• Experience at a firm such as LSEG/Refinitiv, FactSet, S&P Global, Bloomberg, Morningstar or Visible Alpha.
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• Experience with legacy-to-cloud data migration, ideally moving financial datasets off older production systems.
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• Experience with Snowflake or other cloud data sharing platforms.
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• Familiarity with AI data licensing, LLM use cases for financial data or MCP.
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• Experience preparing datasets for third-party data marketplaces.
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• Familiarity with older platforms such as OpenVMS is a plus but not required.
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Who You Will Work With

Enterprise Sales on prospect and client calls and the technical inputs to pricing. Data Operations on production, samples and delivery. Technology on APIs, platforms and modernization. Research on content and methodology. Legal and Finance on licensing, contracts and data costs.