Data & Analytics Engineer

REKOM Group are seeking an experienced Data Engineer to lead the design, implementation and management of a centralised and scalable data platform. This role is critical to the business, and you will help define the data strategy that transforms how we access and utilise information across the Group.

As the Data & Analytics Engineer, you will be responsible for the foundation of our data stack – platform and pipeline architecture to data modelling. You will act as the technical counterpart to the Business Systems team, ensuring that they have clean, governed and structured data to drive reporting and visualisation throughout the business.

This is your chance to gain international exposure and lead a pivotal Group-wide data transformation initiative.

Key Objectives & Responsibilities

Strategy & Platform Architecture

• Platform Ownership & Infrastructure: Own the end-to-end design and implementation of our unified cloud data platform. Utilise Infrastructure as Code to provision and manage resources, ensuring a scalable, secure and reproducible environment.
• Data Strategy & Roadmap: Help define the roadmap for data maturity, transitioning the business from various manual workflows to proactive, automated intelligence for tactical reporting and data driven decision making. Architect a modern multi-layered data structure to serve as the blueprint for the data transformation.
• Cost & Resource Management: Along with the Technical Team, proactively manage cloud compute and storage resources. Implement distinct warehouses / clusters, auto-scaling policies and budget alerts to maximise performance efficiency while preventing unchecked spend.
• Governance & Compliance: Establish a unified governance framework. Implement a centralised data catalogue, strict role-based access control (RBAC), row / column-level security and data retention policies to ensure full data compliance and internal standards.

Data Integration, Engineering & DevOps

• Silo Elimination & Data Ingestion: Identify isolated data pockets across the business and unify them into the central platform. Design robust ELT / ETL pipelines (using Python, SQL or Spark) to ingest high-volume data from internal systems, APIs and flat files.
• Modernisation & Migration: Lead the technical migration of core reporting and data assets from our legacy Data Warehouses to the new modern stack, ensuring data integrity, history preservation and zero downtime for business users.
• Process Automation: Eliminate manual interventions in reporting and transformation. Implement continuous ingestion / delivery pipelines to automate testing, version control and code deployment.
• Pipeline Orchestration: Architect reliable data workflows to manage complex dependencies and ensure timely data delivery.

Data Modelling & Quality

• Semantic Layer Design: Partner with the Business Systems Team to design optimised data models, ensuring seamless integration with downstream BI tools.
• Business Logic Standardisation: Implement complex business rules within the transformation layer. Ensure consistent definitions of critical KPIs across all departments to create a Single Source of Truth.
• Data Quality & Optimisation: Implement automated data quality checks to prevent bad data from entering the serving layer. Continuously optimise table structures for query performance.

Business Expansion & Scalability

• Cross-Functional Support: Expand the platform’s capabilities beyond core Finance / Operations to support specialised needs in our Digital, Commercial and Trade Marketing departments, ensuring the architecture supports diverse data types and use cases.
• Group Integration Strategy: Lead the data integration strategy for Group Companies. Architect the platform to securely handle multi-tenant or multi-entity data requirements, ensuring separation of concerns while enabling consolidated Group reporting.

Technical Skills & Qualifications

Core Engineering & Languages

• Advanced SQL: Expert-level proficiency in writing complex, optimised queries for data manipulation and analysis.
• Python Proficiency: Strong functional programming skills in Python for building pipelines and automating tasks.
• Data Modelling: Deep understanding of dimensional modelling concepts and modern data warehouse design patterns.

Modern Data Platform Experience

• Platform Architecture: Strong understanding of cloud-native data platforms, specifically the separation of compute and storage and how to configure resources to balance performance with cost efficiency.
• Storage Optimisation: Proven ability to design data layouts for high-speed querying using platform-specific optimisation techniques.
• Governance & Security: Proficiency in architecting unified governance frameworks, including Role-Based Access Control (RBAC), Row-Level Security and data masking to ensure compliance.
• Advanced Engineering & Code: Strong capability in combining SQL with Python-based frameworks to build complex, programmatic data transformations.
• Modern Data Lifecycle: Hands-on experience with advanced features such as snapshots for data recovery and handling both batch and streaming data patterns.
• Transformation & Orchestration
• Data Build Tool: Strong experience using dbt for the transformation layer (cleaning, testing and documenting data models).
• Orchestration Tools: Experience designing and scheduling workflows using tools like Airflow, Prefect or platform-native tools.

Infrastructure & DevOps

• Infrastructure as Code: Practical experience with IaC tools to deploy cloud resources programmatically rather than manually.
• CI/CD & Version Control: Expertise in Git flows and building automated deployment pipelines to move code from Dev > Test > Prod.
• Cloud Fundamentals: Solid understanding of a public cloud infrastructure (e.g. Microsoft Azure, AWS), specifically regarding IAM, object storage and networking basics.