How the Brickbuilder Partner Network changes demand generation
Databricks announced the Brickbuilder Partner Network on February 11, 2026. It unifies partners under Bronze, Silver, Gold and Platinum tiers, launches specializations across 6 core industries and 4 core products including GenAI and Lakeflow, and recognizes individual practitioners with Delivery Expert badges. Funding scales with tier, from proposal-based MDF for Silver partners to strategic co-investment funds for Platinum partners.
The incentive design matters more than the tier names. Velocity is built around consumption and runs in three stages. Source rewards partners for sourcing new use cases and greenfield accounts. Activate provides SPIFFs for driving early adoption. Grow pays recurring consumption rebates as customers expand. Each stage maps to a part of the funnel a marketing program can influence, and the first stage is almost pure demand generation.
That makes the Databricks ecosystem unusual. In many partner programs, marketing is something a partner funds and hopes to recover through services margin. Here, a discovery campaign that surfaces a real use case can be rewarded by the vendor, supported by MDF at Silver and above, and then continue to pay through consumption if the use case goes live.
Writing a Brickbuilder MDF proposal that gets approved
Proposal-based MDF means the proposal is the product. Approvers want to see that the money creates Databricks consumption through real use cases, reaches accounts that matter to the field team, and produces evidence. A proposal built around a webinar with no account list, or a sponsorship with no follow-up plan, is easy to reject and hard to report on.
Build the proposal around a Source-phase outcome instead. Name the accounts, the industries and the products in scope, describe the discovery mechanism, and commit to specific outputs. Offer the reporting format up front, and align the account list with the Databricks sellers who own those territories before you submit.
Be realistic about the numbers. A discovery campaign at 100 well-signaled accounts will not produce 100 use cases, and an approver who has read many proposals knows it. Conservative targets that you then beat build more credibility than ambitious targets you miss. Split the reporting into leading indicators, such as accounts engaged and workshops held, and lagging indicators, such as registered opportunities and use cases that reach production. The lagging numbers take longer, and showing both prevents a strong program from being judged too early.
- Objective: new use cases and greenfield accounts, stated as numbers you can report.
- Audience: 40 to 150 named accounts, with the signal that qualified each one.
- Activities: industry webinar, discovery outreach and a roundtable, each with line-item costs.
- Outputs: workshops held, use cases identified, opportunities registered.
- Evidence: attendee lists by account, workshop outputs, sent logs, invoices and meeting records.
What transfers from our data and analytics work
Our data ecosystem experience includes BangDB, where the go-to-market included an SI motion with analytics consultancies, and Quills AI, where the strategy pivoted toward system integrators that cross-sell the product per client instance. Both taught the same lesson: data consultancies respond to peers who understand delivery economics, and the best pipeline often comes through partners who need your capability for part of a larger program.
For a Databricks partner, that suggests running two motions at once. Direct discovery campaigns at end customers create Source-phase use cases. A partner-to-partner motion with larger SIs, cloud specialists and software vendors brings in delivery work on programs someone else has already won. Across the agency, programs like these have produced up to $10M in pipeline per client, with about $2.4M in average sales closed per client account per year.
The human layer is what makes either motion work. Data and AI leaders are skeptical of outreach, and they can tell quickly whether the sender has built a pipeline or shipped a model. We put a named practitioner behind every first message, keep the offer specific, such as a two-hour workshop on one use case, and route replies to a person who can answer technical questions the same day. Your consultants are the most credible marketing asset a Databricks partner has. Most firms keep them hidden until the pitch, which wastes the advantage.
Signals that a Databricks use case is forming
Use cases leave traces before a formal project exists. We combine several signals and require at least two to agree before an account enters a discovery campaign. The Databricks field team's view of consumption trends and stalled projects is the strongest signal available, and worth asking for every quarter.
Our September 2026 list also shows how often Databricks and Snowflake meet in the same account: JPMorganChase, UnitedHealth Group, Citi, Capital One, U.S. Bank and Lantheus carry requisitions for both platforms. In those accounts, lead with a specific use case and its business outcome rather than a platform argument.
Once an account qualifies, match the message to the signal. An open engineering requisition calls for a capacity or pipeline modernization offer. A new data and AI leader calls for a use-case prioritization workshop, because new leaders need early wins. A public AI commitment without production results calls for a GenAI production readiness review. Accounts that do not respond move into a nurture track with useful content and a planned re-approach date, rather than being cycled through the same sequence until they stop listening.
- Open Databricks engineer or architect requisitions that stay open for weeks.
- New or changed chief data and AI leadership.
- Public AI commitments without matching production announcements.
- Legacy Hadoop or warehouse renewal dates.
- Regulatory reviews involving models, lineage or reporting.
Databricks partner marketing terms, defined
The Brickbuilder Partner Network, launched in 2026, introduced new tiers, incentives and funding options. These definitions reflect the program as described on this page. Confirm current details with your Databricks partner manager or in the partner portal.
- Brickbuilder Partner Network: Databricks' 2026 partner program for consulting and technology partners.
- Bronze, Silver, Gold and Platinum: the network's tiers, with funding options increasing by tier.
- Specializations: recognized expertise across 6 core industries and 4 core products.
- Delivery Expert badges: recognition for individual practitioners rather than firms.
- Proposal-based MDF: market development funds available from Silver, requested through an approved plan.
- Strategic co-investment funds: funding available to Platinum partners.
- Velocity: the consumption-centered incentive model with Source, Activate and Grow stages.
- Source: rewards for sourcing new use cases and greenfield accounts.
- Greenfield account: a company not yet using Databricks.
- Lakehouse: an architecture combining data lake storage with warehouse-style management and analytics.
- Discovery workshop: a scoped session identifying a use case, its data requirements and a next step.
- Activate: SPIFFs that reward early adoption of Databricks products in an account.
- Grow: recurring consumption rebates paid as customers expand their use of the platform.
- Use case registration: recording a sourced use case with Databricks so credit is tracked.
Benchmarks to track in a Databricks discovery program
A Databricks partner program has three audiences for its numbers: your leadership, the Databricks field team and whoever approves your next MDF proposal. Tracking one set of benchmarks for all three keeps reporting consistent and makes each proposal easier to justify. Set targets before the campaign starts and review them monthly.
Data companies often need developer credibility and executive business cases at the same time, as BangDB showed with a 12K developer community and a public benchmark. Our partner MDF guide covers funding across ecosystems.
- Named accounts in the program, split between greenfield targets and existing customers.
- Share of accounts engaged by data or AI leaders each month.
- Discovery workshops held and use cases identified per workshop.
- Use cases registered and the time from workshop to registration.
- Opportunities and pipeline created, by industry and use case type.
- Webinar and roundtable attendance from target accounts, not total signups.
- MDF approved, spent and claimed, with evidence complete at claim time.
- Conversion from workshop to paid assessment or project.
- Consumption growth in accounts where your team delivered a use case, reported to the Databricks field team each quarter.
- Share of workshops that included both a business sponsor and a technical data owner.