Role overview
Employment type: Full-time
Pay: $135,000 - $165,000/year
Work model: Remote.
About the role
Build and operate the data infrastructure behind internal analytics and customer-facing sustainability insights. This hands-on role combines ownership of a production data lake and Postgres warehouse with graph database integration, reliable analytics pipelines, and support for generative AI workflows.
You will help consolidate systems developed through acquisitions, establishing consistent schemas, documented data lineage, and clear ownership across the data environment.
What you will own
- Operate and evolve a Postgres data warehouse, including schema design, performance, access controls, and analytics-ready datasets.
- Manage a CDC-fed Apache Iceberg data lake with bronze, silver, and gold layers, including streaming ingestion, the Trino query engine, and the table catalog.
- Monitor pipeline latency, detect schema drift, reconcile source data, and maintain infrastructure, backups, and disaster recovery capabilities.
- Maintain Dagster-orchestrated dbt pipelines, including scheduled and sensor-triggered builds, data quality tests, and branch-based versioning for curated releases.
- Operate BI and reporting systems with consistent metric definitions and per-user access policies enforced at the query engine.
- Integrate graph databases with the warehouse, lake, and primary databases through canonical data models and a shared write path.
- Partner with data science to migrate legacy graph services and evolve relational structures into graph-native models.
- Extend production AI workflows involving embeddings, similarity search, document extraction, and classification.
Technical experience you will need
- 5+ years in data engineering, analytics engineering, or data platform engineering.
- Advanced SQL and experience with databases such as Postgres and MongoDB.
- Hands-on production experience with graph databases and their integration with warehouses and data lakes.
- Experience with open table formats, medallion lake architectures, and distributed SQL engines such as Apache Iceberg, Trino, or Presto.
- Experience with streaming and CDC pipelines using technologies such as Kafka, Pulsar, Debezium, or Flink.
- Strong Python skills for pipelines, automation, and operational tooling.
- Experience with dbt and orchestration tools such as Dagster or Airflow.
- AWS infrastructure experience covering EKS, VPC, IAM, and S3, along with infrastructure as code, CI/CD, GitOps, and Docker.
- Experience with analytics data modeling, metric definitions, automated monitoring, and data quality controls.
- Experience supporting production data systems through incident triage, root-cause analysis, runbooks, and reliability improvements.
- Ability to collaborate with analytics and other stakeholders using Agile workflows and tools such as Jira and Confluence.
- Familiarity with PII protection, encryption, and access management.
Additional strengths
- Experience with BI tools supporting per-user, policy-based access, such as Superset with SSO impersonation.
- Experience with policy-based access control and identity platforms such as OPA and Keycloak.
- Familiarity with multi-region data residency, including separate EU and China data handling.