# René Pretorius

**Director, Product Management · Data Platform & Product Leader · Player-Coach** · Fuquay-Varina, NC

## Target roles (Feanix cluster)

- Director, Product Management (first dedicated product leader, player-coach)
- Head of Product / product organization builder
- Data platform product management (internal + engineering customers)
- Platform PM for APIs, data products, contracts, abstractions
- PM/EM pair leadership model; product and engineering scale as one system
- Engineering-adjacent product leader: spec-driven delivery, agentic coding, verification harnesses
- Decision platform / decision-support products for traditional industries
- Agriculture, farm operations, agtech, dairy-adjacent operational data (crop + retail + equipment)

## Core skills — product management (posting language)

product management, product organization, grow product management from founding-PM mode into a function, first dedicated product leader, player-coach, personally PM the data platform, product strategy, roadmap, prioritization, discovery, documentation practices, lightweight product process, fast-moving team, hiring plan, role definitions, hire and develop PMs, direct reports, product function builder, stand up product function, first PM who defined how product worked, managing and developing PMs, structured product practices, startup and mid-size company, translate CEO vision into execution, accountable owner of product side of strategy, contribute to product strategy with CEO and CTO, clear specs, clear tradeoffs, clear decisions, strong written communication, obsess over end customer, farm visits, trace platform decisions to customer experience, bridge legacy systems to modern products, legacy dairy management platform pattern (equipment + ERP handoff stability), partner APIs, integrations, external consumers, decision engine, justified decisions farmers act on, decision-support, measure adoption and friction, platform as real product, internal customers engineering bioscience agents, prioritize roadmap by value to farms, farm onboarding fast and flexible, absorb per-farm variation without one-off builds, scale farms under management, onboarding without rework, new products without rework, contracts and abstractions, consumed by agents as readily as engineers, LLM-driven world, agentic automated product management, AI tooling daily, rethinking how software gets built

## Core skills — data platform & engineering (posting language)

data platform, data products, APIs, infrastructure products, platform engineering, ingestion from farm systems, pipelines, farm records, equipment files, genomic-scale data processing pattern (high-volume sequencing-like pipelines), core data model, source of truth, cleaned joined model-ready data, ETL/ELT, data engineering, data-intensive systems, complex data, sensor data, large real-world datasets, geospatial datasets, John Deere Operations Center, ERP integrations, warehouse contracts, data contracts, equipment intake, field readiness, quality gates, QC frameworks, verification harnesses, regression testing, production data validation, lineage, knowledge-graph lineage, soft-delete integrity, platform mandates scale onboarding, 100% validation historical output, farm onboarding pipeline, replant detection, two-track migration, incremental exports, live success rates, PostgreSQL, BigQuery, Cloud Run, GCS, Pub/Sub, Python, SQL, GeoPandas, Looker, FME, GitLab CI, full-stack data products, ML-flavored backend pattern (metrics churn models grain definition), LLM-backed product surfaces, dashboard contracts, internal platform-as-product

## Core skills — agentic / AI-native delivery (Engineering Manager posting)

agentic coding, AI-native quality processes, spec-driven development, verification harnesses, development processes for engineers working through agents, quality as property of the system not heroics, leverage point is spec and harness not line-by-line review, plan-mode specs before code, direct agents on implementation, verify against live farm data, AI-first bar, verification fluency, decision system defend output, build through agentic development, software development rebuilt around agentic coding, strong specs before and around code, testing release discipline, daily load-bearing AI use, LLMs agents automation daily tools

## Core skills — leadership & people (mapped to attested work)

player-coach, manage and grow engineers pattern, stood up data-products function, ~60% company revenue, hired developed team, zero turnover, 3–4 person team at scale, headcount nearly flat while load increased, department build from scratch, first management layer pattern, team processes from scratch, quality practices from scratch, cross-functional delivery engineering marketing support agronomy sales, Farm Success Farm Ops partnership pattern (ops + customer success + platform), conflict resolution cross-functional, goals and measurement, honest feedback, calm under pressure, high-growth hiring, promote from within, concurrent roadmaps

## Core skills — agriculture & domain

agriculture, agricultural industry, farm operations, crop farming, agricultural retail, growers, farm intelligence, soil-lab ingestion, field-boundary management, planting harvest equipment files, multi-polygon fields, geospatial decision-support, traditional sector digital transformation, unglamorous sectors, equipment OEM, farm economics decisions, operational data products, living products vs printed reports, customer retention, retailer manufacturer loyalty, YC-style startup pace pattern (high growth small team)

## Preferred / adjacent (attested mapping only)

decision platforms, decision-support products, statistics fundamentals via metric grain churn modeling, machine learning fundamentals via production models and QC not genomic ML, agriculture broadly not dairy-specific, internal platform to external product pattern via retailer-facing insights, bioinformatics genomics exposure omitted unless attested — complex biological-adjacent farm data pipelines only

## Experience

### Lead Data and Platform Engineer — GROWERS
**Feb 2026 – Present**

- First hands-on owner of farm-operations **data platform** product: production pipeline from equipment files to trusted field records; **personally PM** platform contracts (customer definition, metric grain, what platform owes each new farm)
- **Farm onboarding without one-off builds**: equipment intake, field readiness, quality gates, cleaning, replant detection; scale path for 40 full-season growers (~4.6B raw points, ~1-month onboarding when auto-pipeline completes)
- **Bridge legacy systems**: John Deere Operations Center integration ~44% → ~93% live success; two-track plan for existing vs new farms; stability of delivery to operators
- **Agentic spec-driven delivery**: plan-mode specs, agents on implementation, **verification harnesses** against real grower files and historical cleaning; 100% validation across 12 datasets (~27M+ raw rows)
- **Platform as product for internal customers**: loyalty/rewards data platform; warehouse view contracts; soft-delete, attribution, and spend-vs-tier-identity bugs → written platform tradeoffs; a failure in one data product no longer aborts independent builds of others; QC framework ~80% load reduction
- **Retailer-facing product scoping**: unique-customer definition, engagement, churn, points expiration; caught churn model wrong account grain before ship; dashboard + **LLM-backed** surfaces mapped to data platform
- **Absorb variation without rework**: merged-company warehouse alignment; farm intelligence + loyalty share one set of numbers

### Data Operations Manager — GROWERS
**Oct 2022 – Feb 2026**

- **Built data-products function** generating ~60% company revenue; hired, developed, retained team (zero turnover); **player-coach** defining how the function operated
- **Decision-support products** replacing 95% printed reports; customers act on data instead of waiting on ops
- Discovery, scoping, delivery with engineering, marketing, support; **roadmap and prioritization**; knowledge-graph lineage for single definition of numbers
- **Complex farm data at scale**: ~2.9B raw John Deere points / 33 growers; ~90% geospatial automation; >60% manual reduction; 3–4 person team, headcount nearly flat
- Retailer insights: customer base, loyalty strategy, retention — **justified decisions** sales and agronomy teams use

### Senior GIS Specialist — GROWERS
**Sep 2020 – Oct 2022**

- Geospatial **decision-support** tools; workflow automation; discovery with cross-functional partners before engineering commit

### Manager, GIS & Environmental — GreenGo Energy US, Inc.
**Jul 2017 – Sep 2020**

- **Built department from scratch** (role definitions, workflows, standards); ESRI field platform; Python automation; **investor reporting**; small team vs large load

## Education

- MS Geographic Information Systems — University of Redlands
- BSc Honours Geoinformatics — University of Pretoria