Senior Data Engineer

SENIOR DATA ENGINEER Location: Remote - US About Worldly Worldly is the world's most comprehensive impact intelligence platform — delivering real data to businesses on impacts within their supply chain. Worldly is trusted by 40,000 global brands, retailers, and manufacturers to provide the single source of ESG intelligence they need to accelerate business and industry transformation. Through strategic and meaningful customer relationships, Worldly provides key insights into supplier performance, product impact, trends analysis, and compliance. When a company wants to change how business is done, we enable that systemic shift. Backed by a dedicated global team of individuals aligned by values, Worldly proudly operates as a public benefit corporation with backing from mission-aligned investors. Want to learn more? Read our story io/about. About the Opportunity 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. In this role, you will: - 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. What You'll Do Data Warehouse & Data Lake - 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. Orchestration, Transformation & Reporting - 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. Graph Integration - 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. GenAI/NLP Enablement - Support and extend production genAI workflows — embeddings/similarity search, LLM-based extraction and classification — and keep our data infrastructure "AI-ready. " We'd Like to See - 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).