Senior Data Engineer at Momentum Metropolitan Holdings Limited

Role Purpose    

• The Senior Data Engineer is responsible for delivering and evolving data engineering capabilities that enable trusted analytics, reporting, and insight generation across Momentum Investments. The role focuses on the design, build, and operational support of scalable data pipelines and data platforms, ensuring data is secure, high-quality, and readily available to downstream consumers. 
• Operating within an Agile delivery environment, the Senior Data Engineer works closely with fellow Data Engineers, analytics and BI teams, Product Owners, and business stakeholders.
• Beyond hands-on engineering, this role carries accountability for technical direction within the team, supporting design decisions, uplifting engineering standards, and guiding the use of AI capabilities in a controlled, governance-aligned manner to improve delivery effectiveness

Requirements    

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related discipline. Relevant certifications are beneficial. 
• 4–7+ years of professional experience in data engineering or related roles. 
• Demonstrated experience delivering production-ready data pipelines and platforms. 
• Strong exposure to AWS-based data architectures. 
• Hands-on experience working within Agile or SAFe delivery environments. 
• Exposure to implementing AI tooling or capabilities within engineering workflows. 
• Demonstrated ability to apply AI responsibly in engineering work, including validation of outputs. 
• Practical experience using AI to support data pipeline understanding, optimisation, testing, or documentation. 
• Advanced Python and SQL capabilities. 
• Strong foundation in data modelling, analytics-oriented schema design, and data warehousing principles. 
• Experience ingesting data from relational databases, cloud storage, APIs, and file-based sources. 
• Proficiency with Git-based version control, CI/CD pipelines, and automation. 
• Familiarity with analytics and BI tools such as Power BI. 

Duties & Responsibilities    

Delivery Ownership within Agile Execution: 

• Contribute to the delivery of data engineering outcomes aligned to sprint goals and program-level commitments. 
• Actively engage in Agile ceremonies, contributing to planning, estimation, prioritisation, and continuous improvement discussions. 
• Decompose data features into implementable tasks and provide reliable effort estimates. 
• Ensure outputs meet agreed functional, performance, and data quality expectations. 

Data Engineering & Pipeline Development: 

• Design and implement data ingestion and transformation pipelines across multiple systems and data domains. 
• Build solutions that support scalable batch and incremental processing patterns. 
• Ensure robustness of pipelines through appropriate error handling, monitoring, and alerting. 
• Implement data validation and reconciliation mechanisms to maintain confidence in data assets. 

Platform Design & Architectural Consistency: 

• Design data solutions that align with Momentum Investments’ data platform strategy and target architecture. 
• Contribute to the ongoing evolution of the cloud-based data environment (AWS-aligned). 
• Assess the impact of design choices on security, performance, cost, and supportability. 
• Identify integration points, upstream/downstream dependencies, and potential risks early in the delivery lifecycle. 

Technical Leadership, Coaching & Enablement: 

• Provide guidance and technical oversight to less experienced data engineers. 
• Support analytics, BI, and data science teams with clarity on data structures, availability, and pipeline behaviour. 
• Encourage sound engineering judgment, curiosity, and continuous learning within the team. 
• Actively contribute to defining shared standards, patterns, and best practices. 

Engineering Quality & Standards: 

• Review data engineering code and configuration to uphold consistency, reliability, and maintainability. 
• Drive improvements through optimisation and simplification of existing pipelines and data models. 
• Apply disciplined engineering practices including version control, automated testing, CI/CD, and structured releases. 
• Ensure solutions are documented sufficiently for operational support and future changes. 

AI-First SDLC Adoption (Governance-Led): 

• Promote responsible use of AI to enhance data engineering productivity and solution quality, within Momentum Group and Momentum Investments governance frameworks. 
• Use only enterprise-approved AI tooling in line with secure development and AI governance policies. 
• Ensure that no sensitive, proprietary, or client-related information is exposed to public or unapproved AI platforms. 
• Validate all AI-assisted outputs prior to use and retain accountability for correctness, compliance, and production readiness. 
• Identify practical, low-risk opportunities to embed AI support across design, development, testing, and documentation activities. 

Applied AI in Data Engineering (Practical Enablement): 

• Use AI to support understanding of complex data flows, transformations, and lineage across the platform. 
• Apply AI assistance to propose improvements to pipeline logic, performance, and resilience (subject to validation). 
• Leverage AI to aid query formulation, data exploration, and test scenario creation. 
• Support documentation and onboarding efforts by accelerating the creation of technical explanations and data references. 
• Suggest incremental AI-enabled improvements to team practices, aligned to governance and security expectations. 

Operational Support, DevOps & Incident Response: 

• Contribute to data platform operational readiness, deployment pipelines, and monitoring capabilities. 
• Assist in diagnosing and resolving data-related incidents and failures. 
• Participate in root cause analysis and implement corrective actions to improve platform stability. 
• Support operational prioritisation processes and response protocols where required. 

Security, Risk & Compliance: 

• Build data solutions with privacy, access control, and regulatory considerations embedded by design. 
• Identify data risks and contribute to mitigation actions. 
• Support remediation of audit findings, security issues, and compliance gaps. 
• Raise delivery or platform risks proactively and contribute to mitigation planning. 

Stakeholder Engagement & Communication: 

• Communicate technical progress, constraints, and decisions clearly to business and technical stakeholders. 
• Collaborate effectively across technology and analytics teams. 
• Promote constructive teamwork and positive contribution to organisational culture. 

Documentation & Delivery Governance: 

• Maintain accurate technical documentation covering pipelines, data models, and operational considerations. 
• Adhere to delivery governance, change management, and release processes. 
• Ensure work tracking and status updates are accurately reflected in Jira and related tools. 

Competencies    

• Strong analytical thinking and problem-solving ability. 
• Clear communication skills and ability to engage with both technical and business stakeholders. 
• Experience coaching or guiding other engineers. 
• Strong focus on maintainable engineering solutions and technical debt management. 
• Awareness of data security, privacy, and governance obligations. 
• Sound understanding of risks associated with AI-assisted development and how to manage them through engineering controls. 

Closing Date    

• 2026/05/02