W
Worldly
Senior Data Engineer
Skills
SQL QueriesMongoDBData EngineeringDockerPythonData SciencePostgreSQLKafkaData AnalysisAIAWSJiraConfluenceApache AirflowTerraform
1 month ago
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Important information
Salary:
$135,000 – $165,000 / year
Location:
United States
Work mode:
Remote
Published on:
20 September 2026
What they need
Who you are
- 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)
- Experience with BI tooling supporting per-user, policy-based data access (e.g., Superset with SSO impersonation)
- Experience with policy-based access control (e.g., OPA) and identity platforms (e.g., Keycloak)
- Familiarity with multi-region data residency (e.g., separate EU/China data handling)
What the job involves
- 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."
Benefits
- Total Rewards: Earn a competitive salary and performance-based bonuses. Get healthcare, retirement matching, and equity
- Remote Enabled: Use the office stipend to get the supplies you need. Combat zoom fatigue with no-meeting Fridays
- Flexible Time-Off: Take the time you need to recharge. Our culture encourages team members to explore and rest to be their best selves
- Connection: We're remote, not lonely. Join the culture committee, coffee chats, or chess club
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