Data Engineer, Analytics

seRevel; New York (NY); Remote; USD 120,000 - 180,000; Full time

Job summary

seRevel is seeking a data-focused engineer to own ingestion pipelines and Postgres models that turn POC activity, Salesforce data, Gong transcripts, and Jira/Confluence content into actionable numbers for SE leadership.

You will define and ship metrics behind use-case intelligence, ensure data quality end to end, and document models to enable future work on a remote team.

Qualifications

• Experience building production data pipelines and warehouse models.
• Strong SQL and Postgres experience; performance on large tables.
• Familiar with APIs, rate limits, pagination, backfills, and schema drift.
• Able to translate vague sales questions into defined metrics.
• Clear writer; works well async on a remote team.

Responsibilities

• Build and run ingestion pipelines pulling data into one analytical store.
• Design Postgres models turning events into clean facts about POCs, use cases, customers, and outcomes.
• Define and ship the metrics behind use-case intelligence: which use cases predict success and lift win rates.
• Structure transcript and ticket data for AI insights and coaching, not just dashboards.
• Build the analytics SE leaders open every week and defend the numbers when challenged.
• Own data quality end to end: freshness, completeness, dedup across connectors, and handling schema drift.
• Document the models so a small team can build on them later.

Skills

SQL

Postgres

TypeScript

Node.js

Python

APIs

Data pipelines

Data modeling

Job description

Use-case intelligence is the part of seRevel that tells an SE leader which use cases predict a win, which combinations lift win rates, and which ones have quietly degraded. That data does not exist in any CRM, because it comes from what actually happened in the POC, captured as it happens, not typed in from memory a week later. You will own the pipelines, the Postgres models, and the metrics that turn POC activity, Salesforce data, Gong transcripts, and Jira and Confluence content into numbers an SE leader will bet a quarter on.

What you will do

• Build and run the ingestion pipelines that pull POC activity, Salesforce data, Gong transcripts, and Jira and Confluence content into one analytical store, and keep them running when a vendor changes its API.
• Design the Postgres models that turn raw, organically captured events into clean facts about POCs, use cases, customers, and outcomes.
• Define and ship the metrics behind use-case intelligence: which use cases predict success, which combinations lift win rates, which ones are dragging, and how long a POC should really run.
• Structure transcript and ticket data so the AI worker can use it for insights and coaching, not just dashboards.
• Build the analytics SE leaders open every week, and stand behind the numbers when someone who has run a hundred POCs challenges them.
• Own data quality end to end: freshness, completeness, dedup across connectors, and what happens when a source changes.
• Document the models so a small team can build on them without asking you.

What we are looking for

• Experience building and running data pipelines and warehouse models in production, where they have to keep working after you ship them.
• Strong SQL and solid Postgres experience: schema design, indexing, and query performance on large tables.
• Comfortable writing pipeline and transformation code in TypeScript on Node. A Python background is fine if you are willing to switch.
• Hands-on with third-party APIs (CRMs, call recorders, ticketing tools): rate limits, pagination, backfills, and schema drift.
• Can take a vague question from a sales leader and turn it into a metric that is defined, measurable, and honest.
• Clear writer who works well async on a remote team.

Nice to have

• Experience with unstructured text at volume: call transcripts, tickets, meeting notes, and preparing it for LLM pipelines.
• Time spent in or near a Sales Engineering, RevOps, or sales analytics team.
• Predictive or scoring models built on sales data, and a clear view of where they go wrong.