Senior Data Engineering Manager

The Senior Manager of Data Engineering is a technical subject matter expert and organisational leader who drives strategic direction and delivery through people management and thought leadership. Collaborates closely with other technology and business leaders on the data engineering development plan and leads the capacity planning for its execution. Staying close to the various projects supported by their organisation, the incumbent guides the overall direction of their team members’ work, provides technical mentorship for their decisions, and identifies horizontal opportunities for creating leverage across projects. They lead identification data engineering skill gaps within their team and drive development of tools, materials, and training to bridge these gaps.

Core Responsibilities and Accountabilities

Data Strategy and Modeling

• Translate business needs into data solutions by creating logical data models in support of both short- and long-term objectives

• Define semantic architectures by identifying key entities and relationships, and developing them into mature data taxonomies

• Support the data requirements of new and existing solutions by developing scalable and extensible physical data models that can be operationalised within the company’s workflows and infrastructure

• Drive efficiency by mapping data flows between systems/workflows across the company

• Meet varied data consumption patterns by designing solutions for both streaming and batch processing

• Ensure standardisation by enforcing data governance requirements in your team’s work

Data Platform and Pipeline Management

• Oversee the entire life-cycle of a portfolio of data processing systems and platforms

• Guide teams through development of extensible data pipelines spanning different data encodings, protocols and unstructured data across different systems

• Enable rapid data-driven decisions by designing efficient and scalable data ingestion

• Drive high-value data by identifying disparate datasets from different systems to be combined into a well-managed unified solution

• Drive end-to-end accountability for data applications by defining, monitoring and adjusting SLIs and SLOs

Quality, Reliability, and Compliance

• Drive data accuracy by developing criteria, automation, and processes for data production, transport, transformation, and storage

• Drive data validation strategies

• Ensure ongoing reliability of data pipelines by directing standards for end-to-end testing

• Enforce compliance with data-related requirements by building solutions in line with all applicable standards and regulations

• Ensure ongoing resilience of data processes by guarding system performance and ensuring teams identify bottlenecks, potential risks, and failure points that degrade quality

Engineering Standards and Development

• Oversee the build of software applications using the relevant development languages and knowledge of the systems, services and tools used by the specific business area.

• Ensure readable and reusable code by applying standard patterns and standard libraries.

• Choose the right technology by researching and understanding requirements

Craft & Team Development

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