| We have an opening for a Senior Data Engineer who will lead the design, implementation, and optimisation of data infrastructure to align with our client’s strategic objectives.
About the Team & Role:
Our client’s mission is supported by a data ecosystem that drives informed decision-making and innovation. The Data Team is critical to this mission, enabling advanced analytics, machine learning, and actionable insights to shape business strategy and outcomes.
This team – comprising data engineers, data scientists, and data analysts – works collaboratively to transform raw data into impactful solutions, ensuring scalability, reliability, and actionable insights.
As a Senior Data Engineer, you will build scalable, high-performance data pipelines and drive best practices in data engineering.
In this role, you will mentor junior team members, contribute to architectural decisions, and ensure the delivery of robust data solutions that empower data scientists and data analysts to generate insights.
You will also play a key role in defining the future of our data platforms, leveraging advanced technologies to streamline data processing and enable business growth.
Key Responsibilities:
- Lead the design and implementation of scalable and secure data architectures to support analytics and machine learning initiatives.
- Develop and optimise complex, high-volume data pipelines and databases to process structured and unstructured data in real time or batch.
- Ensure data pipelines and databases are optimised for performance, security, availability, and scalability.
- Work closely with data scientists and data analysts to ensure they have access to the right data and that data is formatted for their analysis needs.
- Develop and enforce standards and best practices for data quality, documentation, quality control, security, and compliance across all data processes.
- Evaluate and implement new tools, technologies, and frameworks to improve data systems and processes.
- Serve as a technical leader, coach and mentor for junior team members and adjacent data and engineering teams.
- Lead end-to-end data engineering projects that support strategic goals. This includes requirements gathering, technical deliverable planning, output quality control and stakeholder management.
- Contribute to the development of the data strategy.
Requirements:
- A Bachelor’s degree in Computer Science, Engineering, or a related field. Advanced degrees are a plus.
- 5 – 8 Years of experience as a Data Engineer or in a similar technical role, with a strong track record of building scalable solutions.
- Expertise in programming languages such as Python, Java, or Scala.
- Advanced proficiency with SQL and database optimisation techniques.
- Experience with data warehousing solutions (e.g. Databricks, Snowflake, Redshift, BigQuery, Synapse).
- Experience with distributed systems and big data technologies like Hadoop, Spark, Flink, or similar.
- Strong knowledge of cloud-based data platforms and their services (e.g. AWS, Azure, GCP).
- Experience with ETL tools and frameworks (e.g. Apache Kafka, Apache Airflow, Apache Spark etc.).
- Experience in designing and implementing data architectures that support large-scale data processing.
- Ability to work effectively with cross-functional teams, including data scientists, analysts, and business stakeholders.
- Experience leading and mentoring junior data engineers and other team members
- Experience contributing to and shaping internal data science standards and best practices.
- Strong problem-solving and critical thinking skills, with the ability to identify and resolve data pipeline or infrastructure issues.
- Strong interpersonal skills, with the ability to communicate effectively across technical and non-technical teams.
- An innovative, collaborative mindset with excellent communication skills to work across teams.
Bonus Points & Other Considerations:
- Experience working in the retail or consumer analytics industry.
- Familiarity with embedded and edge computing platforms.
- Knowledge of data security best practices and compliance requirements (e.g. GDPR, SOC2).
- Knowledge of integrating machine learning models into data pipelines or architectures.
- Experience with DevOps practices and tools, including containerisation (e.g. Docker) and infrastructure-as-code (e.g. Terraform).
- Relevant certifications in cloud platforms or data engineering (e.g. Google Professional Data Engineer, AWS Certified Big Data – Specialty, Microsoft Certified Azure Data Engineer).
Benefits:
- Competitive base salary
- Bi-Annual Key Performance Objectives (KPO) Bonus Scheme
- Employer-sponsored provident fund (% of base salary will be discussed)
- Employer-sponsored Discovery health plan (% of base salary will be discussed)
- Hours and place of work are determined by operational requirements (currently remote).
|
