[Hiring] Senior Data Engineer @Revelio Labs

Role Description

We’re hiring a Senior Data Engineer to help design, build, operate, and improve Revelio’s core data flow systems. This is not a narrow ETL role. You will own production data systems end to end:

• Source ingestion
• Transformation logic
• Orchestration
• Infrastructure integration
• Data quality
• Stakeholder communication
• Long-term system design

You will work on systems where correctness and timeliness matter, but where the environment is inherently messy. The right person will be excited by that ambiguity.

What you’ll do:

• Own and operate production data flow pipelines end to end: ingestion, transformation, enrichment, validation, publication, and monitoring.
• Build and improve reusable flow patterns for high-volume datasets such as profiles, companies, job postings, and related enrichments.
• Debug production failures across multiple layers: source data, code, orchestration, storage, Kubernetes, data warehouses, models, and product expectations.
• Investigate data correctness issues, including unexpected or anomalous metrics and client-facing discrepancies.
• Design and implement guardrails: validation checks, lifecycle checks, monitors, alerts, data contracts, runbooks, and safer release patterns.
• Improve pipeline reliability, performance, and cost efficiency by tuning resources, execution patterns, storage layouts, and orchestration logic.
• Partner with Data Acquisition, Infrastructure, Data Science, Economics, and client-facing teams to understand upstream/downstream dependencies and communicate impact clearly.
• Raise the engineering bar for the team by simplifying brittle systems and building patterns that other engineers can safely reuse.
• Bring strong judgment to ambiguous situations: know when to patch, when to roll back, when to rerun, when to escalate, and when to redesign.

Qualifications

• 4+ years of professional experience in a production software or data engineering role.
• Strong programming ability in Python, Go, and/or Rust.
• Strong SQL skills and a proven ability to debug complex data quality issues.
• Strong expertise in building or operating large production data pipelines.
• Experience with incremental processing, idempotency, data versioning, schema evolution, and operational recovery.
• Comfort working across the full data system stack: source inputs, transformation code, workflow orchestration, storage, containers, Kubernetes, data warehouses, and downstream serving layers.
• High agency and ownership: when something breaks or looks wrong, you form hypotheses, gather evidence, reason about blast radius, pull in the right people, and drive toward resolution.
• A systems mindset: you naturally ask yourself how a local change affects upstream producers, downstream consumers, operational load, future debugging, and team maintainability.

Requirements

• Experience with Flyte, Argo Workflows, or similar workflow orchestration systems.
• Experience with Snowflake, ClickHouse, or other analytical data platforms.
• Experience with Terraform, AWS, and production infrastructure workflows.
• Experience with large-scale web-derived datasets, job postings, workforce data, marketplace data, profiles, company data, or other noisy external data sources.
• Experience integrating machine learning or NLP models into production data pipelines.
• Experience with entity resolution (people, postings, or companies), taxonomy systems, or other data normalization problems.

Benefits

• Compensation: The pay range for this position in New York City is $150,000 - $220,000 per year.
• Base pay offered may vary depending on job-related knowledge, skills, and experience.
• Eligibility to participate in the company’s equity program.
• Benefits include medical, dental, vision, retirement, and others.

How to reach us

Please email your resume to [email protected] as a PDF file. Please include your GitHub and highlight any projects that you’ve worked on that may be relevant.