Hard Skills (Technical and Management Competencies)

1. Requirements Engineering and Solution Scoping (New)

• • Data Solution Design: Ability to sit with the client during the discovery phase, understand the business problem, and, together with architects, define the technical scope (MVP vs. Ideal Solution).
  • Proposal and RFP Structuring: Skill in drafting clear technical proposals, detailing assumptions, deliverables, schedules, responsibility matrices (RACI), and technical effort estimates.

2. Data & Analytics and AI Ecosystem

• • Data Pipeline and Architecture: Understanding of Data Lakes, Lakehouses, and ETL/ELT processes to validate whether the proposed architecture makes financial and operational sense for the client.
  • Traditional and Generative AI (LLMs/RAG): Conceptual technical knowledge to question and validate the feasibility of an AI solution. The SDM needs to know if the client's LLM demand requires zero-to-one development, Fine-Tuning, or a RAG architecture, as the cost and timeline for each proposal change drastically.

3. Governance, Compliance, and Pricing (Reinforced)

• • Data and AI Governance by Design: Include LGPD (data protection), data quality, and AI explainability pillars already in the commercial proposal design, selling this as a competitive differentiator for the consultancy.
  • FinOps and Project Pricing: Ability to estimate not only team hours (software/data factory) but also cloud consumption costs (AWS, Azure, GCP) and LLM API usage, ensuring the proposal does not start with a loss for the consultancy.

4. Delivery Methodologies and Contract Models

• • Know how to size proposals for different commercial formats: Time & Materials (Open Scope/Agile) or Fixed Price (Closed Scope), knowing how to mitigate the risks of each model in data projects.

5.Management of Active Contracts/Projects (Cost, Margin, and Revenue)

• • Perform physical tracking and management of projects (Costs, Margins, and Revenue).
  • Ensure agreed-upon deliveries.
  • Evaluate and mitigate project risks.
  • Control Scope and negotiate changes when necessary.

Soft Skills (Behavioral and Relational Competencies)

1. Consultative Active Listening (The "Salesperson's Ear")

• • Ability to listen to the client during the pre-sales phase and read "between the lines." Often, the client asks for a next-generation Generative AI, but their real pain point is the lack of structured basic reports. The SDM needs to capture this to direct the ideal proposal.

2. Facilitation and Collaborative Leadership (SDM + Architects)

• • Ability to lead the team's architects. The architect focuses on technical perfection; the SDM needs to mediate this with the client's budget reality and timeline. The SDM guides the technical team so that the proposal is innovative but highly viable and profitable.

3. Expectation Management and Scope Alignment (Hype Factor)

• • Know how to protect the consultancy already in the proposal. In AI and data, clients often expect "magic." The soft skill here is the ability to transparently establish in the proposal what the technology can actually deliver, avoiding future frustrations and the famous scope creep (uncontrolled scope expansion).

4. Commercial Vision, Storytelling, and Negotiation

Skill in Data Storytelling to defend the technical proposal in client purchasing committees. The SDM does not just present "technology"; they present how the data and AI architecture will reduce costs or generate revenue for the client.