Mactores

• Lead end-to-end database migration initiatives, including assessment, planning, schema conversion, execution, and post-migration validation. • Design, configure, and manage AWS DMS and SCT environments while ensuring migration quality, performance, and reliability. • Author comprehensive migration runbooks that define execution procedures, validation checkpoints, rollback plans, and operational readiness activities. • Perform database migration assessments, dependency analysis, compatibility reviews, and migration strategy recommendations. • Collaborate with infrastructure, application, security, and platform teams to ensure successful migration outcomes. • Drive migration testing activities, including functional validation, performance testing, failover testing, and production readiness reviews. • Provide technical leadership and guidance to migration teams while ensuring adherence to AWS migration best practices and governance standards. • Lead conversion and refactoring of procedural database code — Oracle PL/SQL (packages, procedures, functions, triggers, cursors) and SQL Server T-SQL (stored procedures, functions, triggers) — into PostgreSQL/Aurora PostgreSQL PL/pgSQL, resolving semantic and behavioral differences identified by SCT.

• Take founder-written product notes and decompose them into epics, stories, and engineering-ready work items. • Combine epics into releases with realistic dates. Defend the dates against scope creep and against unrealistic compression. • Maintain the release plan as a living document. When reality shifts, the plan shifts with it, transparently. • Write the acceptance criteria for every epic. What "done" looks like is your call, anchored to the product note. • Define release readiness: what must be true before a release ships. Test coverage, behaviour-equivalence verification, governance hooks, customer-facing documentation. • Sign off on epics before they go to release. No epic ships without your acceptance. • Make day-to-day prioritization calls within a release: which bug first, which epic blocks which, what gets cut when the math doesn't work. • Escalate to founders only when a decision crosses the strategic line. Inside that line, you decide. • Review the product every single day. Use it like a customer would. Find the issues before customers do, and route them to the right engineer. • Maintain a running quality bar that the team can point at. "Would I demo this today?" is the test. • Own the scrum calls: planning, daily stand-ups, retrospectives. • Own the release process end-to-end: cut, validate, ship, post-release review. • Coordinate with DevOps and engineering on the deployment pipeline. Keep it clean and repeatable. • The founders set the release. You ship it. • Surface blockers and risks with enough lead time to mitigate. No surprises on release day. • After every release: what shipped, what landed, what didn't, what needs iteration. Write the retro, share it, and apply the lessons.

• Build and ship specialized agents in the Aedeon fleet: parsers, business-rule extractors, dependency mappers, test synthesizers, behavior replayers, and the orchestration that wires them together. • Own the full delivery of assigned agents from prototype through deployment and post-release validation. • Practice test-driven development. Write the tests for the agent's contract, governance constraints, and equivalence checks before the agent code that satisfies them. • Write clear, maintainable Python with adequate documentation. Review pull requests thoroughly and provide constructive feedback. • Document agent contracts, prompt structures, decision logic, and verification approaches in Confluence or equivalent.

• Build extraction pipelines from SAP HANA to AWS S3 using SLT, ODP, CDS views, SDI, and native HANA SQLScript, picking the right tool per source and per latency requirement. • Model raw SAP tables across FI/CO, MM, SD, and adjacent modules into clean, semantically meaningful datasets that the downstream Spark layer and business users can actually use. • Design and operate delta and CDC patterns so incremental loads stay correct, idempotent, and replayable. • Write ABAP extractors where standard SAP tooling falls short, and document them so future engineers can change them safely. • Own the write-back path: load curated data from S3 into SAP BW / BW4HANA and model it for end-user reporting and analytical querying. • Land data in S3 as Parquet with sane partitioning, schemas, and IAM scoping, and define the contract with the PySpark engineer at the ingestion-to-transformation boundary. • Embed with a customer team, ship the pipeline to production, and stay close enough through cutover to know it actually runs.

• Build non-production SAP HANA environments on AWS. • Install SAP HANA, apply patches, configure system parameters, and tune each environment. • Set up backup, recovery, and basic high availability for non-production systems. • Manage transports, user administration, and role provisioning across the SAP system landscape. • Troubleshoot performance, connectivity, and configuration issues across SAP and HANA. • Document build steps and runbooks for engineering teams. • Coordinate with US-based engineering leads during PST hours.

• Take ownership of recruiting for 2 positions • Understand how to filter and scan resumes Use the system to log profiles and collect interview feedbacks • Set up calendar invites for – 3 Rounds of interviews with the hiring panel. Assist in document verification, background check process with the team • Create and release offer letters to newly appointed members. • Ensure joining formalities and induction is done as per training

• Use services such as Sales Navigator, Crunchbase, Instantly, Apollo, and Contact Out to find and verify potential leads' emails and phone numbers. • Proficiently use automation services like HubSpot and Instantly to automate emails and voicemails, and SMS campaigns. • Craft compelling and personalized emails, SMS, and voicemail scripts using ChatGPT. • Launch, monitor, and analyze the performance of email, SMS, and voicemail campaigns. • Contribute to digital lead generation strategies and execute plans to capture and nurture leads. • Maintain and update CRM systems with accurate and up-to-date contact information and activity records. • Generate and present reports on campaign performance, lead generation metrics, and other relevant sales operations data. • Work closely with the sales and marketing teams to align strategies and ensure consistent messaging and outreach efforts.

• Research and fetch complete contact details of leads—including Name, Email Address, Contact Number, LinkedIn URL, City, State, Country, and more, using tools such as Clay, ContactOut, Apollo, Hunter, Lusha, Snovio, and similar data sources. • Ensure all contacts in HubSpot are regularly updated, validated, and enriched with accurate and complete information. • Build, maintain, and optimize HubSpot automation workflows to ensure only relevant and qualified contacts remain in the CRM. • Support data hygiene tasks, including duplicate checks, segmentation, list cleaning, lifecycle stage updates, and contact scoring. • Collaborate closely with the Marketing Team to design and implement outbound email campaigns and configure them within HubSpot and Instantly. • Assist with report generation, dashboards, and CRM insights to support sales and marketing decision-making. • Maintain documentation of prospecting processes, workflow configurations, playbooks, and templates. • Work efficiently with distributed teams and proactively communicate progress, blockers, and improvements.

• Design and implement generative AI solutions using large language models (LLMs). • Apply prompt engineering techniques and build scalable Retrieval-Augmented Generation (RAG) systems. • Fine-tune and optimize models for performance, cost, and reliability. • Leverage AWS services such as Bedrock, SageMaker, and Lambda for deployment and inference. • Develop APIs and backend components for production-grade AI applications. • Implement observability, performance monitoring, and security best practices. • Drive responsible AI adoption through evaluation, bias detection, and compliance.

• Build coverage across AWS US sales territories, account managers, ISMs, partner-development managers, 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 that is 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, and 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 and technical 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; keep the AWS rep in the loop through cycle close. • Feed market signal back to Mactores leadership what AWS reps are hearing from customers, where the partner conversation is shifting, and what messaging is landing.