Head of Data & AI

About the role

Indie Campers is scaling its technology organisation to build an AI‑ready data platform. Reporting to the CTO, the Head of Data & AI will lead Data Engineering, Data Science and Analytics, acting as both people manager and hands‑on technical lead.

Key responsibilities

• Own the end‑to‑end data platform (streaming & batch pipelines, orchestration, CI/CD, observability, cost control).
• Define data contracts, schemas and a semantic/metrics layer for company‑wide KPIs.
• Standardise ELT with dbt, orchestrate with Airflow/Prefect, and implement lake/warehouse patterns (S3/Parquet, Athena/BigQuery/Snowflake).
• Build MLOps foundations (feature store, model registry, evaluation, monitoring, safe rollout/rollback).
• Deliver pragmatic ML/AI solutions such as demand forecasting, price optimisation, propensity scoring and LLM‑powered classification/extraction.
• Partner with Engineering to productionise models as APIs or event‑driven services and measure business impact.
• Lead a small team of analytics engineers and analysts, delivering trusted dashboards (Metabase/Superset/Looker) and running experiments.
• Own data governance, catalog, lineage and GDPR/PII controls, establishing data‑quality SLAs and incident playbooks.
• Collaborate with Corporate IT/CISO on access, encryption, retention and vendor risk.
• Hire, coach and develop the team, set operating rhythms (OKRs, QBRs) and communicate results to executives.

Required profile

• 10+ years of experience in data roles, with at least 3 years leading combined Data Engineering, Data Science/ML and Analytics teams.
• Proven track record building or modernising data warehouses/lakes, streaming and batch pipelines, and metrics layers.
• Hands‑on coding experience (Python, SQL) while also shaping architecture and standards.
• Experience in high‑growth product companies and ability to tie data initiatives to revenue, margin and customer outcomes.

Required skills

• Python
• SQL
• dbt
• Airflow or Prefect
• Kafka, Pub/Sub or Kinesis
• Amazon S3, Parquet
• Athena, BigQuery or Snowflake
• MLOps tooling (feature store, model registry, monitoring)
• Metabase, Superset or Looker
• GDPR/PII data‑governance concepts