Senior Data Engineer

Worldly

. Build, operate, maintain, and evolve systems supporting internal analytics and customer-facing analytics platforms .

Posted 9/17/2026full-timeRemote • United StatesSenior💰 $135,000 - $165,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining data pipelines, particularly with streaming/CDC technologies and graph databases. Proficient in managing data infrastructure, ensuring data quality, and collaborating with cross-functional teams to support analytics initiatives.

Highest-signal resume keywords

Data EngineeringAdvanced SQLGraph Database ExperienceStreaming/CDC PipelinesAWS Infrastructure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentPostgresApache IcebergTrinoPythonDbtTerraformCI/CDData ModelingIncident Triage

Soft Skills

CollaborationProblem-SolvingCommunication

Tools & Technologies

KafkaPulsarDebeziumFlinkDagsterAirflowGitOpsDockerJiraConfluence

Industry Keywords

Data LakeMedallion ArchitectureData SecurityPolicy-Based Access ControlMulti-Region Data Residency

Tech Stack

Tools & technologies

AirflowApacheAWSDockerKafkaMongoDBPostgresPulsarPythonSQLTerraform

About the role

Key responsibilities & impact

Build, operate, maintain, and evolve systems supporting internal analytics and customer-facing analytics platforms
Own the production CDC-fed medallion data lake on Apache Iceberg queried through Trino
Operate and evolve the Postgres data warehouse, including schema, performance, access controls, and analytics-ready datasets
Own CDC ingestion from source databases through message bus, streaming writer, and Iceberg bronze/silver/gold layers
Operate the Trino query layer and table catalog
Own pipeline health, including latency SLAs, schema-drift detection, and source reconciliation
Manage GitOps/Terraform infrastructure and backup/disaster-recovery posture
Establish consistent schemas, documented lineage, and clear ownership across the data estate
Maintain and evolve Dagster-orchestrated dbt pipelines, including sensor-triggered and scheduled builds, data-quality tests, and branch-based versioning
Operate the BI/reporting layer with per-user, policy-based data access and consistent metric definitions
Own pipelines integrating the graph database with the warehouse, lake, and primary databases
Migrate legacy direct-to-graph services onto a shared integration path and evolve relational structures into graph-native models
Support and extend production genAI workflows including embeddings/similarity search and LLM-based extraction and classification
Keep data infrastructure AI-ready
Collaborate with data science and cross-functional analytics stakeholders
Participate in incident triage, root-cause analysis, runbook development, and reliability improvements

Requirements

What you’ll need

5+ years in data engineering, analytics engineering, or data platform engineering
Advanced SQL and relational database experience with Postgres and MongoDB
Hands-on graph database experience in production, including integration with warehouses and lakes
Experience with open table formats and medallion lake architectures such as Apache Iceberg
Experience with distributed SQL engines such as Trino or Presto
Experience with streaming/CDC pipelines such as Kafka or Pulsar, Debezium, and Flink or similar
Strong Python skills for pipelines, automation, and operational tooling
Experience with dbt orchestrated by a modern scheduler such as Dagster or Airflow
AWS infrastructure experience with EKS, VPC, IAM, and S3 via infrastructure-as-code such as Terraform
Experience with CI/CD, GitOps such as ArgoCD, and Docker
Experience with analytics data modeling, metric definitions, and automated monitoring/data-quality controls
Experience operating production data systems, including incident triage, root-cause analysis, runbooks, and reliability improvements
Comfortable working with cross-functional/analytics stakeholders using Jira/Confluence and Agile
Familiarity with data security practices including PII protection, encryption, and access management
Experience with BI tooling supporting per-user, policy-based data access is helpful
Experience with policy-based access control and identity platforms is helpful
Familiarity with multi-region data residency is helpful
Must be located in and legally eligible to work in the United States
Must have 5+ years of hands-on experience in data engineering, analytics engineering, or data platform engineering
Must have worked with graph databases in production and integrated them with data warehouses or data lakes
Must have direct experience building and maintaining data pipelines using streaming/CDC technologies

Benefits

Comp & perks

Medical, Dental, and Vision Insurance through multiple PPO options; employer covers 90% of employee premiums and 60% of spouse/dependent premiums
Company-sponsored 401k with up to 4% match for US employees
Incentive Stock Options
100% Parental Paid Leave
Unlimited PTO
12 paid company holidays
Performance-based bonuses
Office stipend
No-meeting Fridays
Flexible time off
Culture committee, coffee chats, and interest groups
Work-from-home stipends
Occasional travel for business needs