Senior Data Engineer — Remote with AI-Ready Data Lake

Praxy; California (MO); Hybrid; USD 135,000 - 165,000; Full time

Benefits offered by this job

Healthcare

401k match

Equity

Unlimited PTO

Paid holidays

Job summary

Worldly is hiring a hands-on data engineer to build, operate, and evolve systems that power internal analytics and customer-facing dashboards. You will own the data lake (Iceberg), the Postgres data warehouse, and the integration with the graph database, driving improvements in data quality, schema lineage, and access controls.

You’ll extend GenAI workflows and maintain Dagster/dbt pipelines, while collaborating with data science and product teams to deliver reliable, scalable analytics across

Qualifications

• 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 and integrating graph models with warehouses/lakes.

Responsibilities

• Operate and evolve the Postgres data warehouse and analytics datasets.
• Own the data lake end-to-end (CDC, Iceberg, Trino) and the table catalog.
• Maintain Dagster/dbt pipelines with quality tests and versioning.
• Operate the BI/reporting layer with per-user data access controls.
• Integrate graph databases with the warehouse/lake and legacy pipelines.
• Support GenAI workflows and AI-ready data infrastructure.

Skills

Advanced SQL

Python

Data modeling

Cross-functional collaboration

Problem solving

Tools

Postgres

MongoDB

Apache Iceberg

Trino

Kafka

Pulsar

Flink

Dagster

dbt

Airflow

Terraform

Docker

Kubernetes

S3/AWS

Job description

Worldly is hiring a hands-on data engineer to build, operate, and evolve systems that power internal analytics and customer-facing dashboards. You will own the data lake (Iceberg), the Postgres data warehouse, and the integration with the graph database, driving improvements in data quality, schema lineage, and access controls.

You’ll extend GenAI workflows and maintain Dagster/dbt pipelines, while collaborating with data science and product teams to deliver reliable, scalable analytics across