1. Data Engineer / Data Analyst

Job Duties

• Design, build, and maintain large‑scale ETL/ELT data pipelines on Databricks using PySpark.
• Develop production‑ready data models, optimizing performance, scalability, and reliability.
• Transform complex business requirements into analytical datasets supporting finance, retail, operations, and leadership reporting.
• Monitor pipeline performance, troubleshoot issues, and ensure high data availability and quality (99%+ uptime).
• Collaborate closely with BI, data science, and business teams to deliver analytics-ready datasets.
• Implement data governance, documentation, version control, and engineering best practices.
• Build automated workflows for ingestion, transformation, and validation of high‑volume datasets.

Candidate Requirements

• 3-8+ years in data engineering, analytics, or BI engineering roles.
• Strong expertise in Databricks, PySpark, SQL, and delta lake architectures.
• Hands‑on experience designing end‑to‑end data pipelines in cloud environments (Azure preferred).
• Strong understanding of data modelling, performance optimisation, and pipeline orchestration.
• Experience working with large datasets (100M+ rows).
• Ability to translate requirements into scalable technical solutions.
• Familiarity with Fabric, ADF, or other orchestration tools is a plus.

2. Databricks Apps Developer (Streamlit)

Job Duties

• Build full‑stack analytical applications using Streamlit, integrating Databricks compute and data sources.
• Develop backend APIs, transformations, and business logic to support app functionality.
• Design polished, user-friendly UI/UX for both business and technical users.
• Deploy and maintain applications with CI/CD and containerisation when applicable.
• Implement secure authentication, data access controls, and performance optimisation.
• Partner with BI, data engineering, and product teams to deliver fast, stable internal tools.

Candidate Requirements

• Strong hands-on experience with Streamlit for production‑grade applications.
• Solid Python development background with clean, modular coding practices.
• Good understanding of Databricks notebooks, clusters, and data access layers.
• Experience integrating APIs, cloud services, and real‑time or batch datasets.
• Ability to build both backend logic and front‑end layouts.
• Nice to have: Docker, GitHub Actions, deployment on Azure/GCP/AWS.

3. BI Modeler (Power BI / Databricks / Google Analytics)

Job Duties

• Develop robust BI models and datasets using Databricks, Google Analytics, and Power BI.
• Build dashboards and visualisation layers that support executive decision-making across finance, retail, and operations.
• Optimise data models for speed, scalability, and user adoption (e.g., DAX optimization, star-schema modelling).
• Collaborate with business stakeholders to convert insights into measurable business actions.
• Manage BI governance, workspace management, deployment pipelines, and version control.
• Conduct training sessions to help teams adopt self‑service analytics.

Candidate Requirements

• Strong experience with Power BI, DAX, Databricks SQL, and Google Analytics.
• Proven ability to design and optimise semantic models and enterprise dashboards.
• Solid understanding of finance or supply chain metrics (a plus).
• Experience building KPI frameworks and analytics for senior leadership.
• Strong sense of UI/UX for data storytelling and business clarity.

4. Data Scientist (including AI / ML / LLM / RAG)

Job Duties

• Build machine learning and AI models across regression, classification, time‑series, or deep learning.
• Develop LLM-based applications, RAG pipelines, vector search, prompt engineering, and agent workflows.
• Design and deploy production-grade ML systems (MLOps, CI/CD, observability).
• Partner with engineering teams to integrate AI solutions into business processes.
• Conduct research and experimentation to evaluate new AI technologies and models.
• Deploy models using Azure, GCP, or AWS infrastructure.
• Communicate insights to stakeholders, explaining technical concepts in business terms.

Candidate Requirements

• Strong expertise in Python, ML frameworks (PyTorch, TensorFlow, or similar).
• Hands-on experience with LLMs, RAG, vector databases, Langchain, Pinecone, or similar.
• Experience deploying ML systems on cloud environments.
• Solid knowledge in feature engineering, data preparation, and model evaluation.
• Experience with containerisation (Docker) is a plus.
• Bonus: Computer vision, agentic systems, or MLOps experience.