# Data & BI Engineer - Odos at deCircle

> Remote · United States · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/recruitee%3Adecircletalentpartner%3A2314934 (read 2026-11-08).

## Facts (observed)

- Company: [deCircle](https://alderhq.app/companies/decircle)
- Location text: San Francisco, California, United States
- Where it can be done from (inferred): Remote · United States (evidence: "Location: San Francisco, California, United States")
- Posted: 2025-10-17 (387 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://talent.makerdao.network/o/data-bi-engineer-odos
- Skills listed (inferred): Python, PostgreSQL, Redis, SQL

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 1752): p25 $161k, median $200k, p75 $239k

## Description (the employer's text)

### About Odos

Odos is the most advanced Smart Order Routing protocol in DeFi — aggregating liquidity across 16+ EVM chains to deliver the best execution paths for swaps, bridges, and limit orders. With billions in total volume and millions of unique wallets, we’re expanding our data and analytics capabilities to better understand our users, optimize routing algorithms, and support new products like Protected Swaps, Multi-Token Swaps, and Loyalty Programs.

### Role Overview

We are seeking a Data & BI Engineer responsible for the design, reliability, and scalability of Odos’s transaction data pipelines, business intelligence metrics, and Tableau dashboards. This role will bridge on-chain trade data with user and loyalty program analytics, enabling the product, growth, and finance teams to make data-driven decisions.

You will own the transaction database layer and help build a unified analytics view of Odos operations — from trade volume and fee revenue to wallet activity, rewards, and partner performance.

### Responsibilities

### Data Infrastructure

Maintain and scale the Odos transaction and event database (Redis, PostgreSQL, or equivalent).

Build ETL pipelines from on-chain data sources, APIs, and internal services.

Ensure data integrity, consistency, and query performance across chains and order types.

### Business Intelligence & Reporting

Define and track key metrics (e.g., total volume, route count, fee revenue, average slippage, gas savings, wallet activity).

Develop and maintain Tableau dashboards and automated reporting for the leadership team.

Collaborate with product and growth teams to design campaign and feature-specific analytics (Protected Swaps, Multi-Token Swaps, etc.).

### Loyalty & User Analytics

Integrate and analyze loyalty program data — wallet tiers, reward distribution, retention, and ROI.

Work with marketing and growth teams to attribute traffic and measure referral or loyalty campaigns.

### Cross-Functional Collaboration

Partner with backend engineers on event schemas and pipeline performance.

Support finance and ops with data exports for audits, investors, and DAO reporting.

### Requirements

3+ years experience in data engineering or BI development (preferably in fintech, crypto, or trading).

Strong SQL proficiency and familiarity with Redis, PostgreSQL, and time-series data.

Experience with Tableau (or equivalent BI tool) — data modeling, calculated fields, dashboard automation.

Proficiency in Python or similar scripting language for ETL and data analysis.

Familiarity with on-chain data, DEX trade logs, or blockchain analytics tools (e.g., Dune, Flipside, The Graph).

Understanding of DeFi protocols, swaps, liquidity pools, and wallet behavior.

Strong communication skills, ability to turn raw data into clear business insights.

### Nice to Have

Experience integrating blockchain indexers or running custom subgraphs.

Familiarity with The Graph protocol and decentralized data indexing.

Experience designing metrics for revenue forecasting and incentive programs.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/recruitee%3Adecircletalentpartner%3A2314934 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.