Staff Data Engineer

Job not on LinkedIn

🕒 June 27

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

🚰 Data Engineer

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Payabli

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2020

💼 Consulting

📣 Marketing

📦 Logistics

💰 $36M Series B - Payabli on 2025-06

Consulting • Marketing • Logistics

Payabli is a next-generation payments infrastructure company that enables software platforms to embed, manage, and monetize payments. It offers a full suite of capabilities — Pay In (payment acceptance and merchant onboarding), Pay Out (payables and payouts to vendors/partners), and Pay Ops (operations, risk, billing, and reporting) — delivered via APIs, embeddable components, and white-glove advisory services. Payabli targets SaaS companies, financial institutions, tech-enabled services, and enterprise merchants, and is a registered payment facilitator/ISO offering compliant, scalable payments solutions and developer-friendly integration tools.

📋 Description

• Architect the platform. Set our warehouse/lakehouse direction and stand up the data lake and layered architecture that turns our raw system of record into trustworthy, queryable, intelligence-ready data. • Build the pipelines. Design and run batch and streaming pipelines that move data reliably out of our production systems - CDC, ELT, and real-time where it matters. • Model the data. Define the canonical datasets and models the whole company depends on, getting the grain, semantics, and contracts right. • Own reliability and accuracy. This is financial data, so correctness is non-negotiable. You'll own data quality, observability, integrity checks, and the testing and monitoring that let us trust it. • Build for a regulated environment. Design in role-based access, masking, lineage, and auditability from day one, and keep sensitive financial data out of places it doesn't belong. • Enable AI/ML and analytics. Build the feature pipelines and trustworthy data foundation our intelligence work relies on, moving us from systems of record toward systems of intelligence and action. • Set the standard. Establish the practices, tooling, and CI/CD for data that the future team inherits. You're setting the bar, not just clearing it.

🎯 Requirements

• 8+ years building production data systems, with a track record of owning architecture and seeing big decisions through to production. • Expert SQL and strong Python. • Deep experience in at least one modern lakehouse/warehouse ecosystem - for example Snowflake with dbt and Fivetran, or Databricks with Spark, Delta Lake, and Unity Catalog. We care that you've gone deep somewhere and can reason from first principles across stacks, not that you've used a specific product. • Strong data modeling skills - dimensional, normalized, or Data Vault - and a sense for designing models that age well. • Experience with pipeline orchestration (Airflow, Dagster, Prefect, or equivalent) and large-scale processing (such as Spark). • Production experience on a major cloud (AWS, GCP, or Azure), including security and cost patterns. • Experience working with sensitive or regulated data - access controls, encryption, governance, and an instinct for keeping the blast radius of mistakes small. • A high technical bar set through influence and example. You make the work and the people around you better, and you're as comfortable in the codebase as you are in a design review.

🏖️ Benefits

• Competitive salary • Stock options with the potential to unlock more equity as we grow • Flexible PTO and paid parental leave • Medical, dental, & vision insurance • 401K, HSA, pre-tax savings programs