Mactores is the agent-native AWS modernization firm. We ship AWS modernization to production in weeks, data platforms migrated, legacy applications and databases refactored, AI agents running against real data, for mid-market and lower-enterprise companies in financial services, healthcare and life sciences, internet and software, manufacturing, and TMEGS.
Our delivery model is built around AI agents that absorb the repetitive 60–70% of engagement work, discovery, schema mapping, validation, test generation, cutover rehearsal, that traditional consulting bills against human hours. Forward-deployed engineers work on top of those agents, embedded with the customer's team, owning architecture, judgment, and cutover. We commit to a fixed delivery date on a fixed fee. If we slip for reasons inside our control, the cost sits with us.
Most modernization doesn't ship. Ours does. That is the entire pitch.
You'll be the named Mactores contact for AWS sales teams across US territories. AWS account managers, ISMs, partner-development managers, and specialist sellers each carry a partner deck full of firms that all say roughly the same thing. Your job is to make them see Mactores as the agent-native firm in that mix, the one they route to when their customer needs a modernization program finished, not staffed.
What you'll sell maps directly to what AWS reps care about: we're MAP-eligible, AWS Agentic AI Specialized, and predictable on fixed-date engagements. Our founders built the agent platform we deploy, so deployment depth is a structural advantage and AWS reps make money when we win. The conversation starts on the same side of the table.
You won't run deep technical demos, a forward-deployed engineer does that. You set up the meeting, frame the customer's problem, walk through the agent-native approach in business terms, anchor everything in shipped case studies, and hand off to the FDE when the conversation reaches architecture. Then you close the loop: register the opportunity, keep the AWS rep informed, push the deal forward.
This is not a script-and-cadence BDR seat. AWS reps see hundreds of partner BDRs. The ones who get traction can hold a real conversation about the customer's modernization problem, not run through a sequence.
What you will do?
Build coverage across AWS US sales territories: account managers, ISMs, partner-development managers, and specialist sellers (Data, AI/ML, Modernization).
Reach out, follow up, show up at AWS Summits and regional partner events, and build named relationships.
Walk AWS reps through what agent-native AWS modernization means in practice and why it's different from what every other partner in their deck says.
Anchor every conversation in shipped Mactores case studies: named customer, named baseline, named outcome, time-to-value, cost reduction.
Identify customer fit, frame the business problem, articulate Mactores' business value, and tee up the technical demo.
Hand off to the forward-deployed engineer for the architecture walkthrough. Your job is to make sure the FDE walks into a qualified room.
Source Amazon-originated opportunities into the Mactores pipeline, register them in ACE, and keep the AWS rep in the loop through cycle close.
Feed market signal back to Mactores leadership, what AWS reps hear from customers, where the partner conversation is shifting, and which messaging lands.
What are we looking for?
3–5 years in a BDR, SDR, or partner-development role, with a demonstrated ability to source qualified pipeline from a cold start.
Comfort talking to AWS field teams. Prior work at an AWS partner, exposure to AWS Partner Central, ACE, or MAP, and time on the floor at AWS Summits or re:Invent are preferred, not required but they shorten the ramp.
Comfort with the technical concepts of cloud modernization, data platforms, and agentic AI without being the engineer in the room. You can read an architecture diagram, talk through a case study, and name where the customer's pain is. You don't need to debug a Lambda.
Sharp business-value articulation: you can take a messy customer problem statement, restate it in two sentences, and show how Mactores ships against it.
A self-managed work style. Remote role, distributed AWS field, no daily stand-up babysitting your activity.
Travel roughly 30% across the US AWS offices, customer sites, AWS Summits, re:Invent, and regional partner events.
How we work?
We're not selling a roadmap. Our agents run in production today, and our forward-deployed engineers ship with them every day. Mactores is MAP-eligible, AWS Agentic AI Specialized, and holds seven AWS Consulting Competencies and seventeen Service Validations, the credentials that make an AWS rep's routing decision easy.
The agent-native category window is 6–12 months. After that, "agent-native" becomes commodity vocabulary the way "cloud-native" did. We're on the early side of that window, and the BDR who plants the flag in AWS field conversations now will own those relationships when the category gets loud. You'll join an existing partnership and sales motion, the system works. Your job is to add territory coverage and AWS-originated pipeline to it, not invent the function from scratch.
How you'll be measured
A combination of net-new logos and AWS-originated revenue. Not call counts, not email volume, not meetings booked for their own sake. The leading indicator is qualified AWS-sourced pipeline. The lagging indicator is closed-won revenue from customers who came in through the AWS field. Quota structure and targets are discussed in the interview process.
Compensation
Additional Information
Mactores is a trusted leader in providing modern data platform solutions and End-to-End Data Solutions that are automated, agile, and secure. Since 2008, the company has been enabling businesses to accelerate their value through automation and digital transformation. Mactores collaborates with customers to strategize, navigate, and accelerate their digital journey via assessments, migration, and modernization services. The company leverages cloud platforms like AWS and specializes in deploying cutting-edge technologies including generative AI models and large language models to solve real-world business problems.
