Mappa
Senior Data Engineer (AI-First Platform)
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Senior Data Engineer
Full-Time | Remote/Hybrid £60,000
About the Company
We are an AI-native distribution company for creators and media IP, specialising in content optimisation through automation. Our platform intelligently processes raw content, determines optimal posting strategies, and refines performance insights — akin to Palantir for social media in the creator economy.
Organised by content verticals (video, podcasts, and future expansions), each feeds into a unified content and performance graph, growing smarter with each campaign. The team balances YC-style agility with Palantir-level rigor in systems and data, fostering rapid iteration and deep engineering discipline.
Position Overview
We’re seeking a Senior Data Engineer with a passion for AI/ML systems, responsible for designing and scaling data-driven AI infrastructure at enterprise scale. Experience with high-volume, unstructured data pipelines and AI-forward datasets is essential.
Your role: Build and maintain robust data layers capable of ingesting GBs per day of video, image, and performance data, while creating LLM/agent-friendly datasets to power internal AI models.
Key focus areas: In-house social API integrations, scalable data infrastructure, and AI-aligned pipeline optimisation.
Core Responsibilities
1. In-House Social API Integration
- Redesign and own data ingestion pipelines to transition from third-party social APIs to a fully self-managed, scalable solution.
- Engineer API integrations and bridge data gaps for valid, actionable social media signals.
2. High-Volume Data Infrastructure & Architecture
- Scale and optimize data pipelines for massive volumes of unstructured data (video, metadata, performance logs).
- Build fault-tolerant, performance-optimized storage using PostgreSQL for structured data and specialized storage for AV/ML asset tracking.
- Develop scheduling, ETL, and orchestration frameworks to handle concurrent team campaigns without bottlenecking.
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3. LLM & Agent Optimised Datasets
- A unique technical bridge between massive-scale data and internal AI agents.
- Structure and pre-process natural-language, semi-structured, and unstructured datasets (Markdown, text snippets, raw promts, logs) for effective inference.
- Implement data versioning, lineage, and attributes for high-contexting AI agents, enabling ML scenarios like auto-tuning content discovery.
Requirements & Qualifications
Technical
- 5+ years as a Data Engineer with AI/ML adjacent experience, preferably in fast-moving startups or enterprise-grade creative/platform companies.
- Python: Intermediate to advanced, with a bias toward clean, production-grade code and scalability.
- PostgreSQL: Production-ready expertise in optimisation, partitioning, and transactional consistency.
- Data Platforms: Hands-on experience with:
- Orchestration: Airflow, Prefect, or other workload schedulers.
- Batch/Stream: Kafka, Flume, Spark, or cloud solutions (e.g., Dataflow).
- Storage: Variants of Parquet/ORC/CSVs, object storage, and managed DBs (e.g., Snowflake, BigQuery).
- ETL: Batch/near-real-time processes, debatching, rigour in data quality, and clean field transitions.
- AI/LLM Alignment: Prior hands-on experience (or deep curiosity) in tasks like:
- LLM fine-tuning data preparation or dataset design.
- Legal metadata organisation for compliance-intensive AI ingestion use cases.
- Formatting and ingesting SOTA datasets in custom pipelines.
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Creative & Cultural Fit
- English Proficiency: C1/C2 level, or near-native. Must intuitively navigate academic/peer reviews (e.g., design docs, architectural diagrams) and inform non-technical leaders with clarity and excitement.
- Problem-Solving: No deadlines are too hard; no waste tolerated. Think end-to-end when taking ownership of components.
- Startuportology: Thrives in small teams, permanently challenged to scale elegantly, and treat failure as catharsis for growth.
Deliverables & Soft Metrics
- Pipeline latency and uptime: SLA <99.9%.
- Social data metadata quality: >95% field scrubbed and normalized.
- Agent/ML influence test scores: 20% improvement in campaign time-to-accuracy.
Location & Flexibility
Hybrid (UK-Based, South London - WeWork near Waterloo Station)
- Model: 3 days on-site (flexible roster) at office hours with a late workend shift to accommodate US team overlaps.
- Preferred but not mandatory for candidates outside UK (all remote interviews).
Fully Remote (Latam-Based)
- Timezone: Works with Pacific Standard Time (PST), no local office requirement.
Compensation: £60,000 (candidate-grade benches offset via • Early equity • Performance bonus 15% once year-2 hinges (social maturity). Flexible final arrangements (per participation), but no trailing work hours monoxide.
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