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Salary: USD 135,000 - 165,000 / annual
Worldly is hiring a Senior Data Engineer to own and evolve the data systems powering their ESG intelligence platform, trusted by 40,000 global brands. This is a hands-on role with significant technical scope and operational responsibility.
You will own the production data lake end-to-end—a CDC-fed, medallion-architecture lake built on Apache Iceberg, queried through Trino—as well as an independent Postgres data warehouse. Key responsibilities include:
**Data Warehouse & Lake Operations**: Operate and evolve the Postgres data warehouse (schema, performance, access controls) and build analytics-ready datasets. Own the lake pipeline completely: CDC ingestion from source databases through message bus to streaming writers to Iceberg bronze/silver/gold layers. Maintain the Trino query layer and table catalog. Own pipeline health including latency SLAs, schema-drift detection, and source reconciliation. Manage GitOps/Terraform infrastructure, backup, and disaster recovery.
**Orchestration & Transformation**: Maintain and evolve Dagster-orchestrated dbt pipelines with sensor-triggered and scheduled builds, data quality tests, and branch-based versioning. Operate the BI/reporting layer with per-user, policy-based data access enforced at the query engine and consistent metric definitions across dashboards.
**Graph Database Integration**: Own pipelines integrating the graph database with the warehouse, lake, and primary databases within canonical data models and a single write path. Partner with data science to migrate legacy direct-to-graph services onto the shared integration layer and evolve relational structures into graph-native models.
**GenAI/NLP Enablement**: Support and extend production GenAI workflows including embeddings/similarity search and LLM-based extraction and classification. Keep the data infrastructure AI-ready.
You'll work cross-functionally with analytics stakeholders, incident triage and root-cause analysis, and drive reliability improvements across production systems.
**Requirements**:
- 5+ years in data engineering, analytics engineering, or data platform engineering
- Advanced SQL and relational database experience (Postgres, MongoDB)
- Hands-on graph database experience in production, including integrating graph models with warehouses and lakes (core to this role, not peripheral)
- Experience with open table formats and medallion lake architectures (Apache Iceberg) and distributed SQL engines (Trino, Presto)
- Experience with streaming/CDC pipelines (Kafka or Pulsar, Debezium, Flink or similar)
- Strong Python skills for pipelines, automation, and operational tooling
- Experience with dbt orchestrated by a modern scheduler (Dagster, Airflow)
- AWS infrastructure experience (EKS, VPC, IAM, S3) via infrastructure-as-code (Terraform), plus CI/CD, GitOps (ArgoCD), and Docker
- Experience with analytics data modeling, metric definitions, and automated monitoring/data-quality controls
- Experience operating production data systems: incident triage, root-cause analysis, runbooks, reliability improvements
- Comfortable working with cross-functional/analytics stakeholders (Jira/Confluence, Agile)
- Familiarity with data security practices (PII protection, encryption, access management)
**Nice to Have**:
- Experience with BI tooling supporting per-user, policy-based data access (Superset with SSO impersonation)
- Experience with policy-based access control (OPA) and identity platforms (Keycloak)
- Familiarity with multi-region data residency (EU/China data handling)
