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Senior Data Engineer

AI Summary
Worldly is seeking a senior data engineer to own and evolve production data infrastructure supporting internal and customer-facing analytics. The role spans a Postgres warehouse, CDC-fed Apache Iceberg data lake, Trino query layer, graph-database integrations, and governed data pipelines. The engineer will operate Dagster-orchestrated dbt workflows, improve reliability and data quality, and manage AWS infrastructure through Terraform, GitOps, and CI/CD practices. This position also supports production GenAI and NLP workflows, including embeddings, similarity search, extraction, and classification. It requires strong cross-functional collaboration with data science, analytics, and operational stakeholders in a remote US environment.
Remote from
USA
Salary
USD 135k–165k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
8 Dec 2026
Listing views
21
Application actions
1
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Opportunity details

About this role.

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.

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).

  • 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).

It Helps If You Have

  • 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 We Can Offer You

  • Medical, Dental, and Vision Insurance are offered through multiple PPO options. Worldly 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.

Life at Worldly
Our team is motivated to transform the way products are made. By helping our customers succeed in a new era of sustainable production, we can build technology that makes a difference on a planetary level.

Our team represents over 15 countries and brings unique experiences from technology to farming to the table. Surround yourself with kind, enthusiastic, and dedicated people who put collaboration and growth at the center of our shared goals.

Benefits and Perks

  • Earn a competitive salary and performance-based bonuses. Get healthcare, retirement matching, and equity for US employees.

  • 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.

  • We’re remote, not lonely. Join the culture committee, coffee chats, or a variety of other interest groups.

Travel Notice
Roles at Worldly may require occasional travel to support business needs, including but not limited to team collaboration, customer engagement, or company events.

Equity Statement
We believe reflecting the diversity of those we strive to serve is essential. True innovation happens when everyone has room at the table, including the tools, resources, and opportunity to excel. We’re dedicated to building a culturally and experientially diverse team that leads and works with empathy and respect.

Compensation Overview
Annual Base Rate (USD): $135,000 – $165,000
10% Annual Bonus
Incentive Stock Option Package
Work-From-Home Stipends

*Final compensation figures will be determined based on a wide variety of factors, including experience and location. These factors will be evaluated and considered by Worldly throughout the entirety of this process.

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a senior, broad-scope platform role requiring ownership of complex streaming, warehouse, lakehouse, graph, security, and infrastructure systems. Production reliability responsibilities and migration of legacy pipelines add substantial technical and operational complexity.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$150,000
US market range (per year)$140k–$180k
AI insightThe employer explicitly lists an annual USD base-pay range of $135,000–$165,000, with a midpoint of $150,000 per year. The estimated US market range for a senior data engineer with lakehouse, streaming, graph, and cloud-infrastructure responsibilities is $140,000–$180,000 annually; bonuses and equity are excluded from these base-pay figures.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design and operate a CDC pipeline from a production database into an Apache Iceberg lake?

I would use a durable CDC mechanism such as Debezium to publish ordered change events to a message bus, then operate a streaming writer that validates schemas, handles idempotency, and writes partitioned Bronze Iceberg tables. I would promote validated and reconciled data through Silver and Gold layers, with monitoring for freshness, lag, schema drift, duplicate events, and source-to-target reconciliation.

What practices would you use to maintain reliable dbt pipelines orchestrated by Dagster?

I would define clear software-defined assets or jobs, use sensors and schedules only where appropriate, and enforce tests for source freshness, uniqueness, referential integrity, and business rules. I would also implement alerting, lineage visibility, environment-specific configuration, branch-based validation, and documented recovery procedures for failed runs.

How would you integrate graph data with an existing warehouse and data lake without creating conflicting data paths?

I would establish canonical entities, identifiers, ownership boundaries, and a single governed write path. Relational and event data would be transformed through controlled pipelines into graph-ready nodes and edges, with reconciliation checks and versioned model contracts to prevent direct legacy services from creating inconsistent graph state.

How would you enforce policy-based data access for BI users at the query-engine layer?

I would integrate authentication and identity claims with the query engine, map users or groups to policies, and apply row-, column-, and table-level controls centrally rather than relying solely on dashboard permissions. I would audit access decisions, protect sensitive PII through masking or encryption, and regularly test policy behavior using representative user identities.

Describe your approach to supporting production embedding and LLM extraction workflows in a data platform.

I would treat these as production data products with versioned inputs, model and prompt metadata, quality metrics, and retriable processing. I would build secure pipelines for document ingestion, embedding generation, vector indexing, and result evaluation, while monitoring cost, latency, drift, failures, and access to sensitive source content.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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