Senior Full Stack Data Engineer

Hive Science is a tech start-up whose technologies are in use by many of the world’s largest brands including Land Rover, Edward Jones & numerous other fortune 500 brands. The Hive platform is delivering novel intelligence at the intersection of quantitative social psychology, behavioral science & AI/ML to accelerate & transform how work is done. We’re looking for someone to join our team and get on board this rocket ship with us..

You'll help design & drive the data infrastructure behind Hive's enrichment products: how Hive intelligence is joined to external client &partner datasets. You'll design &build extensible, repeatable systems and integration capabilities to allow for the same core intelligence to append to partner & customers' data. You'll join the product team and work alongside our existing data engineering team, which runs Hive's core platform.

You'll move quickly, build what's needed and harden it for production

• Build infrastructure that ingests, links, transforms, enriches and delivers Hive intelligence across external data providers and customers, with bidirectional flows in each destination's required format.
• Build repeatable onboarding for customer first-party data, covering intake, validation, standardisation and integration with Hive's existing data architecture.
• Design for rapid onboarding of new datasets, schemas, grains and keys, with clear boundaries between core platform and partner-specific configuration.
• Build reusable record linkage, key mapping, schema mapping, data contracts, validation and output generation across audience, segment and geographic grains.
• Automate QA for coverage loss, join-rate changes, schema drift and geographic mismatches, with lineage from source to final output.
• Partner with our data engineering team to strengthen shared infrastructure and pipelines for reliability, monitoring and scale.

• Production data platforms with high-volume ETL/ELT, distributed processing and orchestration (e.g. Airflow, Dagster, Prefect), as distinct from analytics pipelines.
• Python and SQL at a high level, plus Bash automation.
• Cloud infrastructure, with AWS preferred (S3, Lambda, ECS, IAM);
  Snowflake and/or Databricks;
  Docker; CI/CD (Git Hub Actions or similar);
  Infrastructure-as-Code (Terraform or Cloud Formation).
• Record linkage and entity matching across heterogeneous datasets.
• Data clean-room set-up and integration (Snowflake, AWS Clean Rooms, Info Sum, Databricks or similar).
• Geospatial data engineering: spatial SQL, geographic indexing, Census geographies, crosswalks and large-scale spatial joins.
• Database design and scaling: SQL, No
  
  SQL, vector DBs (Pinecone, FAISS), graph DBs (Neo4j).
• Privacy and security engineering: aggregation thresholds, suppression rules, role-based access, encryption at rest and in transit, secrets management, lineage and audit.
• Automated external data acquisition (APIs, feeds, scraping) built for continuous updates rather than one-off pulls.
• Monitoring and alerting across models and infrastructure (Cloud Watch, Prometheus/Grafana, Sentry or similar; MLOps).
• AI and ML integration: product ionising models (embeddings, LLMs); orchestrating LLM and NLP pipelines; ETL for training and inference data.
• Fluency with Claude Code: you use AI coding agents daily, and they make you meaningfully faster.
• External data providers, consumer or audience data, or data marketplaces
• A degree in Computer Science, Engineering, Mathematics or a related field, or equivalent experience.

• Synthetic data generation and validation for testing and privacy-preserving development.
• MCP integration with data sources and agents.
• Frontend basics (React/Type Script) for internal tools and QA dashboards.
• Early-stage or high-growth startup…