Senior Python Engineer

Block MB; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Block MB is seeking senior Python backend engineers to own data-heavy query and search systems at scale in a fast-growing data intelligence platform. You will build production APIs in Python/FastAPI and optimize queries on billions of records.

You'll shape the data layer, own the query language, and collaborate across teams while mentoring juniors. Experience with ClickHouse, PostgreSQL, and large datasets is essential, with a bias toward fast, reliable analytics.

Qualifications

• > Strong Python backend experience in recent roles (FastAPI or async frameworks).
• > Built/owned data-heavy backends serving hundreds of millions to billions of records.
• > Designed or owned search/aggregation layers (Elasticsearch/OpenSearch/ClickHouse).
• > Deep SQL and Postgres skills with performance improvements.

Responsibilities

• <b>The core scope</b> is data-heavy backend systems and the query and search infrastructure.
• <b>Scale</b> with billions of rows, fast filtering and aggregation.
• <b>Production APIs</b> in Python/FastAPI powering client features.
• <b>Data pipelines</b> integration to make new data queryable quickly.

Skills

Python back-end

Data-heavy systems

Query/Search infra

Postgres optimization

Production APIs

Leadership

LLMs / AI

ClickHouse

TypeScript basics

Tools

ClickHouse

PostgreSQL

Elasticsearch/OpenSearch

FastAPI

Job description

Our client is a profitable, bootstrapped B2B data intelligence platform that hundreds of professional teams rely on every day to find and assess companies. We are seeking highly skilled Senior Python Backend Engineers to own the systems that query, filter and search billions of records and return answers in real time.

The backend team is doubling from two to four. The two current engineers have carried the platform through rapid product growth, and the next stage depends on making very large datasets fast, searchable and reliable.

What you'll build:

The core of this role is data-heavy backend systems and the query and search infrastructure on top of them.

• Query and search at scale: fast filtering, search and aggregation across billions of rows in a column-oriented analytics database, alongside Postgres for application data.
• The query layer: own the filtering and query language that every user interaction runs through, from design to performance tuning.
• Performance work: profile slow queries, design indexes, partitioning and data-access patterns, and keep response times low as data and usage grow.
• Production APIs: Python/FastAPI services that power the client-facing product, internal tools and AI agent features.
• Integration with data pipelines: clean boundaries between ingestion from many data sources and the serving layer, so new data becomes queryable quickly.

What we're looking for:

We need clear evidence that you have built and run data-heavy systems at scale. Your CV should show the volumes you worked with and the query or search systems you owned.

• Strong Python backend experience as your main language in recent roles, ideally with FastAPI or another async framework.
• Data-heavy systems at scale: you have built backends serving large datasets, typically hundreds of millions to billions of records, and can say what the volumes and latency targets were.
• Query or search infrastructure: you have designed or owned search, filtering, aggregation or query layers, for example with Elasticsearch, OpenSearch, Solr, ClickHouse, Druid or similar.
• Deep SQL and Postgres skills: query plans, indexing, partitioning and real performance improvements you can describe with numbers.
• Production APIs that other teams depend on, built and owned end to end.
• High ownership: comfortable taking a broadly scoped problem and driving it without detailed specs.
• Hands-on experience with ClickHouse or another column-oriented database in production.
• Experience at a data or market-intelligence platform, or any product whose core value is querying large datasets.
• Building with LLMs or AI agents on top of structured data.
• Leading or mentoring other engineers.
• Some TypeScript or React, enough to work across the stack when needed.