Delivery Ownership within Agile Execution:
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Contribute to the delivery of data engineering outcomes aligned to sprint goals and program-level commitments.
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Actively engage in Agile ceremonies, contributing to planning, estimation, prioritisation, and continuous improvement discussions.
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Decompose data features into implementable tasks and provide reliable effort estimates.
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Ensure outputs meet agreed functional, performance, and data quality expectations.
Data Engineering & Pipeline Development:
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Design and implement data ingestion and transformation pipelines across multiple systems and data domains.
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Build solutions that support scalable batch and incremental processing patterns.
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Ensure robustness of pipelines through appropriate error handling, monitoring, and alerting.
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Implement data validation and reconciliation mechanisms to maintain confidence in data assets.
Platform Design & Architectural Consistency:
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Design data solutions that align with Momentum Investments’ data platform strategy and target architecture.
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Contribute to the ongoing evolution of the cloud-based data environment (AWS-aligned).
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Assess the impact of design choices on security, performance, cost, and supportability.
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Identify integration points, upstream/downstream dependencies, and potential risks early in the delivery lifecycle.
Technical Leadership, Coaching & Enablement:
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Provide guidance and technical oversight to less experienced data engineers.
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Support analytics, BI, and data science teams with clarity on data structures, availability, and pipeline behaviour.
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Encourage sound engineering judgment, curiosity, and continuous learning within the team.
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Actively contribute to defining shared standards, patterns, and best practices.
Engineering Quality & Standards:
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Review data engineering code and configuration to uphold consistency, reliability, and maintainability.
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Drive improvements through optimisation and simplification of existing pipelines and data models.
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Apply disciplined engineering practices including version control, automated testing, CI/CD, and structured releases.
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Ensure solutions are documented sufficiently for operational support and future changes.
AI-First SDLC Adoption (Governance-Led):
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Promote responsible use of AI to enhance data engineering productivity and solution quality, within Momentum Group and Momentum Investments governance frameworks.
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Use only enterprise-approved AI tooling in line with secure development and AI governance policies.
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Ensure that no sensitive, proprietary, or client-related information is exposed to public or unapproved AI platforms.
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Validate all AI-assisted outputs prior to use and retain accountability for correctness, compliance, and production readiness.
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Identify practical, low-risk opportunities to embed AI support across design, development, testing, and documentation activities.
Applied AI in Data Engineering (Practical Enablement):
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Use AI to support understanding of complex data flows, transformations, and lineage across the platform.
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Apply AI assistance to propose improvements to pipeline logic, performance, and resilience (subject to validation).
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Leverage AI to aid query formulation, data exploration, and test scenario creation.
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Support documentation and onboarding efforts by accelerating the creation of technical explanations and data references.
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Suggest incremental AI-enabled improvements to team practices, aligned to governance and security expectations.
Operational Support, DevOps & Incident Response:
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Contribute to data platform operational readiness, deployment pipelines, and monitoring capabilities.
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Assist in diagnosing and resolving data-related incidents and failures.
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Participate in root cause analysis and implement corrective actions to improve platform stability.
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Support operational prioritisation processes and response protocols where required.
Security, Risk & Compliance:
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Build data solutions with privacy, access control, and regulatory considerations embedded by design.
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Identify data risks and contribute to mitigation actions.
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Support remediation of audit findings, security issues, and compliance gaps.
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Raise delivery or platform risks proactively and contribute to mitigation planning.
Stakeholder Engagement & Communication:
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Communicate technical progress, constraints, and decisions clearly to business and technical stakeholders.
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Collaborate effectively across technology and analytics teams.
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Promote constructive teamwork and positive contribution to organisational culture.
Documentation & Delivery Governance:
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Maintain accurate technical documentation covering pipelines, data models, and operational considerations.
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Adhere to delivery governance, change management, and release processes.
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Ensure work tracking and status updates are accurately reflected in Jira and related tools.
