Senior Data Engineer, Worldly

• 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, not peripheral, to this role
• Experience with open table formats and medallion lake architectures (e.g., Apache Iceberg) and distributed SQL engines (e.g., Trino, Presto)
• Experience with streaming/CDC pipelines (e.g., Kafka or Pulsar, Debezium, Flink or similar)
• Strong Python skills for pipelines, automation, and operational tooling
• Experience with dbt orchestrated by a modern scheduler (e.g., Dagster, Airflow)
• AWS infrastructure experience (EKS, VPC, IAM, S3) via infrastructure-as-code (e.g., Terraform), plus CI/CD, GitOps (e.g., 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)

Desirable

• Worldly is hiring a hands-on data engineer with a passion for sustainability to join our dynamic team. You will take on a primary role in building, operating, maintaining, and evolving the systems that support our internal analytics and power our customer-facing analytics platforms
• Own our production data lake — a CDC-fed, medallion (bronze/silver/gold) lake on Apache Iceberg, queried through Trino — as well as our independent Postgres data warehouse
• Play a core role in integrating our graph database with the warehouse, lake, and primary databases, including migrating legacy pipelines onto a single, governed integration layer
• Apply generative AI and embeddings-based techniques already running in production (e.g., entity matching, document extraction) to keep expanding what our data can do
• Operate and evolve our Postgres data warehouse (schema, performance, access controls) and build analytics-ready datasets from it
• Own the lake end-to-end: CDC ingestion (source database → message bus → streaming writer → Iceberg bronze/silver/gold), the Trino query layer, and the table catalog
• Own pipeline health — latency SLAs, schema-drift detection, source reconciliation — and the GitOps/Terraform infrastructure and backup/DR posture underneath it
• Bring consistent schemas, documented lineage, and clear ownership to a data estate that's grown quickly through acquisitions
• Maintain and evolve our Dagster-orchestrated dbt pipelines: sensor-triggered and scheduled builds, data quality tests, branch-based versioning for curated releases
• Operate our BI/reporting layer, including per-user, policy-based data access enforced at the query engine (not just the dashboard), and consistent metric definitions across dashboards
• Own pipelines integrating the graph database with the warehouse, lake, and primary databases, working within canonical data models and a single write path
• Partner with data science to migrate legacy direct-to-graph services onto that shared path, and to evolve relational structures into graph-native models
• Support and extend production genAI workflows — embeddings/similarity search, LLM-based extraction and classification — and keep our data infrastructure "AI-ready."