Data Engineer, Fractal

Salary not provided

SQL

AWS

GCP

Python

Scala

Kafka

Postgres

Airflow

MySQL

Azure

Redshift

Spark

Snowflake

BigQuery

Git

dbt

Senior and Expert level

London

2-3 days a week in office

AI-driven data analytics platform

Be an early applicant

AI-driven data analytics platform

1001+ employees

B2BArtificial IntelligenceAnalyticsSaaSData Analysis

Be an early applicant

Salary not provided

SQL

AWS

GCP

Python

Scala

Kafka

Postgres

Airflow

MySQL

Azure

Redshift

Spark

Snowflake

BigQuery

Git

dbt

Senior and Expert level

London

2-3 days a week in office

1001+ employees

B2BArtificial IntelligenceAnalyticsSaaSData Analysis

Company mission

To reshape business through science and advanced data tools and uncomplicate decision-making, so human intelligence can be freed from the drudgery of monotonous work and committed to the pursuit of imagination and creative rewards.

Job

Company

Role

Who you are

The ideal candidate is passionate about data quality, automation, and solving complex data challenges in a collaborative environment
Experience: 5+ years of professional experience in data engineering or a related role across GCP services
Programming: Proficiency in Python and/or Scala for data processing and pipeline development
SQL: Strong expertise in writing complex, optimized SQL queries for data extraction and transformation
ETL/ELT: Hands-on experience building data pipelines using frameworks like Apache Spark, Apache Airflow, DBT, Fivetran, Matillion, or equivalent
Databases: Solid understanding of relational databases (e.g., PostgreSQL, MySQL) and experience with modern data warehousing solutions (e.g., Snowflake, BigQuery, Redshift, Synapse)
Data Modeling: Knowledge of data modeling principles (e.g., star schema, snowflake schema, normalization)
Version Control: Experience with Git and collaborative development workflows
Problem-Solving: Strong analytical and problem-solving skills with a focus on data quality and system reliability
Communication: Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical stakeholders

What the job involves

We are seeking a skilled and detail-oriented Data Engineer to design, build, and maintain scalable data pipelines and infrastructure
You will be responsible for transforming raw data into reliable, high-quality datasets that fuel analytics, reporting, and machine learning initiatives
Design & Build Pipelines: Develop, deploy, and monitor robust, scalable, and efficient ETL/ELT data pipelines using modern tools and frameworks
Data Modeling & Architecture: Design, implement, and optimize data models (relational, dimensional, NoSQL) in data warehouses, data lakes, or lakehouses
Data Integration: Ingest, process, and integrate data from diverse sources (Kafka, pub-sub, databases, APIs, streaming platforms, SaaS applications, flat files)
Data Quality & Governance: Implement data validation, cleansing, and monitoring processes to ensure accuracy, consistency, and reliability of data assets
Infrastructure & Optimization: Manage and optimize cloud data infrastructure (e.g., GCP, AWS, Azure) and on-premise systems for performance, cost-efficiency, and scalability
Collaboration: Partner closely with Data Analysts, Data Scientists, and business stakeholders to understand data requirements and deliver solutions that meet their needs
Automation & CI/CD: Automate data pipeline deployments, testing, and monitoring using CI/CD principles and tools
Documentation: Maintain clear and comprehensive documentation for data pipelines, models, and processes
Troubleshooting: Investigate and resolve data pipeline failures, performance bottlenecks, and data quality issues

Application process

End Date: December 31, 2025

Salary benchmarks

Our take

The analysis of business data is essential in making operational changes based on forecasting, managing supply chains, and optimizing revenue, but the mass of available data can be overwhelming. This can sometimes be combatted with the power of AI-driven data analysis tools, but existing solutions are usually designed to solve specific problems within specialized industries.

Fractal develops AI-driven tools and platforms for businesses, including a conversational AI platform called Senseforth, a revenue growth AI tool called Samya, AI algorithms for medical imaging named Qure, and an all-in-one data analytics platform called Crux Intelligence. The company develops its solutions to be flexible and applicable within a wide range of industries including consumer packaged goods, healthcare, financial services, and media.

Whilst AI-driven business tools are commonplace, Fractal has taken the smart approach of developing a suite of flexible AI tools that can tackle problems in many industries, as opposed to focusing on a specific problem or industry. This strategy has positioned the company well for continued growth and impact, reflecting its broad applicability and potential in the market.

Steph

Company Specialist at Welcome to the Jungle

Insights

8% employee growth in 12 months

Glassdoor (4.1)

Company

Funding (last 2 of 4 rounds)

Jan 2022

$360m

GROWTH EQUITY VC

Jan 2019

$200m

GROWTH EQUITY VC

Total funding: $685m

Company benefits

Health, dental, vision and life insurance
Disability plans
401(k)
11 paid holidays
12 weeks parental leave
PTO policy

Company HQ

World Trade Center, New York, NY

Leadership

Pranay Agrawal

(CEO)

Experience as Associate for ANZ Grindlays Bank. Manager at ICICI bank from 1998 to 2000.

Srikanth Velamakanni

(Executive Vice Chairman)

Experience as Assistant Manager of ICICI Bank. Co-founder and Trustee of Plaksha University since 2018.

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