Data Analyst/Data Engineer

Role Overview

Our client is currently building a Sports Intelligence Platform and seeking a sharp and intellectually curious individual to join their early-stage team. This role sits between a Data Analyst and a Data Engineer — more technical than a traditional analyst, yet focused on sourcing, structuring, and making sense of external data rather than building full-scale engineering infrastructure.

You will be responsible for identifying, acquiring, extracting, cleaning, and structuring data from diverse public and proprietary sources. The ideal candidate is highly resourceful, concise in thinking, and comfortable working in a fast-paced startup environment with high expectations.

You will work closely with the Founder and contribute directly to data-driven product and strategic initiatives.

1. Data Discovery & Research

• Identify relevant public and proprietary data sources across the web.
• Evaluate paid data providers where appropriate.
• Conduct structured research across varied domains (e.g., business databases, academic journals, medical publications, sports data, niche industry sources).
• Determine what data is required, where it can be found, and how it can be obtained.

1. Data Extraction & Acquisition

• Scrape and extract data from web sources using automation scripts.
• Access and integrate proprietary or paid data sources.
• Set up and manage API connections to retrieve structured datasets.
• Work with various tools and scripting methods to automate data collection processes.

1. Data Processing & Structuring

• Clean, normalise, and structure raw datasets.
• Build lightweight data pipelines to prepare data for downstream application use.
• Ensure reliability, accuracy, and usability of collected data.

1. Data Analysis & Validation

• Analyse datasets to validate hypotheses or research findings.
• Support case-based research assignments, including:
• Identifying datasets that support or challenge research papers.
• Building basic analytical models to validate findings.
• Extract meaningful insights from disparate datasets.

What We’re Looking For

• 2–5 years of relevant experience (up to 7 years maximum).
• Strong experience in web scraping and automated data extraction.
• Experience working with APIs and integrating external data sources.
• Exposure to proprietary/paid data platforms is a plus.
• Startup experience preferred.
• Experience setting up data infrastructure for consumer applications is highly advantageous.
• Background in companies whose core value comes from data aggregation, scraping, or intelligence (e.g., data platforms similar to Lusha, Apollo, etc.) is a strong plus.

• Proficiency in scripting for data extraction (e.g., Python or similar).
• Ability to automate workflows for large-scale data collection.
• Strong data cleaning and structuring capabilities.
• Ability to build basic analytical or validation models.

• Thinks in structured frameworks:

1. What data is required?
2. Where can it be found?
3. How can it be obtained?

• Strong research instincts and intellectual curiosity.
• Comfortable exploring non-obvious data sources and testing different search approaches.
• Able to work across varied domains depending on the problem at hand.

• Sharp, concise thinker and communicator.
• High intellectual agility.
• Comfortable working in a high-expectation, low-structure environment.
• Thrives in early-stage, fast-moving teams.
• Self-directed and execution-oriented.

• Work directly with the Founder on high-impact data initiatives.
• Exposure to diverse problem domains.
• Opportunity to build foundational data capabilities in an early-stage environment.
• High ownership and meaningful responsibility from day one.