Data Engineer

Role Overview

This senior technical leadership role owns the vision, design, and ongoing evolution of the company’s enterprise data architecture, data platforms, and AI engineering capabilities. By building scalable, secure, and well-governed data foundations, the position directly enables the business to generate value from high-quality data products and to power advanced analytics, machine learning, and generative AI use cases. The role also safeguards alignment with enterprise architecture standards, governance rules, security controls, and strategic technology goals, while championing reliability, interoperability, and operational excellence across the data landscape.

Key Responsibilities

• Lead the design and delivery of enterprise data architectures, including databases, data warehouses, reporting layers, and integration solutions.
• Build, tune, and maintain big data pipelines, architectures, and datasets that support analytics, machine learning, and generative AI workloads.
• Create and integrate APIs to facilitate reliable data exchange between internal systems and external services.
• Conduct root cause analysis on data and business processes to answer key questions and uncover opportunities for improvement.
• Define and enforce data quality, integrity, and governance standards across all data platforms and products.
• Provide technical direction and mentorship to data engineering teams, promoting best practices in data pipelining and performance optimization.
• Partner with stakeholders to translate business requirements into data solutions that comply with enterprise architecture, security, and strategic objectives.
• Evaluate and adopt emerging tools and frameworks for data pipeline orchestration, workflow management, and AI engineering.

Requirements & Qualifications

• A first degree in Information Technology, Mathematics, Statistics, Business Commerce, or a related discipline.
• 8–10 years of experience building databases, warehouses, reporting solutions, and data integration pipelines.
• Proven track record of constructing and optimizing big data pipelines, architectures, and datasets.
• Hands-on experience creating and integrating APIs.
• Strong capability in root cause analysis across internal and external data and processes to answer business questions and identify improvement opportunities.
• Deep understanding of data pipelining, performance optimization, data principles, and how data supports customers, products, and transactional information.
• Knowledge of integration patterns, styles, protocols, and systems theory.
• Proficiency in database programming languages such as SQL, PL/SQL, Spark, or equivalent data tooling.
• Experience with data pipeline and workflow management tools.
• Professional certifications in cloud data platforms, data architecture, data engineering, data science, machine learning, or AI engineering are advantageous.
• Strong communication skills to articulate complex information clearly, a commitment to developing expertise, the ability to interpret data, manage multiple tasks, deliver outputs, and collaborate effectively within teams.
• Solid technical grounding in big data frameworks and tools, data engineering practices, data integrity, data quality, IT systems, and stakeholder management.