Sobre esta vaga de Data Engineer na First Quantum Minerals
At First Quantum, we free the talent of our people by taking a very different approach which is underpinned by a very different, very definite culture – the “First Quantum Way”.
Working with us is not like working anywhere else, which is why we recruit people who will take a bolder, smarter approach to spot opportunities, solve problems and deliver results.
Our culture is all about encouraging you to think independently and to challenge convention to deliver the best result. That’s how we continue to achieve extraordinary things in extraordinary locations.
Job description:
Company Description
First Quantum Minerals is a leading Canadian-based global mining & metals company focused on the production of copper, nickel, gold & cobalt. As a company, we strive for continuous excellence and after 25 years of operations we are now one of the world’s top 10 copper producers, exporting millions of tonnes of concentrate from multiple countries to customers worldwide. Our operations and future developments span across Africa, Europe, the Middle East, Australia and the Americas, and we are globally recognised for our specialist technical, engineering, construction and operational skills, which allow us to unlock value from complex mineral projects and deliver rewarding careers for our people, returns for our shareholders and sustainable development for the many local communities that host our operations. As we expand our operations, continue to provide metals to build the modern world and shift to a low carbon, greener economy in the years ahead, our mining projects will continue to require the best and the brightest talent to help us solve the emerging challenges of our time, shape our business and unlock opportunities for our future.
Job Description
Although our production and financial results are the engine that drives our business, it is the depth of capability in our people that will continue to determine First Quantum’s ongoing success. Reporting to the Lead, Group Data Operations, this role will form an integral part of our global D&A function and act as the technical authority for data engineering at FQM. Work collaboratively within an integrated team of Data Engineering, Data Designers, Data Scientists, Database Administrators, DevOps Engineer and Data Architects, this role will be responsible for designing, developing, and maintaining data pipelines and systems in the Azure cloud environment, ensuring the smooth deployment and vigilant monitoring of data solutions. As such, success will be measured not by pipelines built, but by a service that is sustainable, repeatable, performant, and trusted by the business.
What success looks like in year one
- Measurably improved SLAs across the Data Engineering Service.
- Automation and refined processes across DevOps and the Engineering Service.
- The reputation of the Data Engineering Service is demonstrably high within the business.
- Increased data literacy and engineering capability across data operations.
- A strong, functioning relationship with the Centre of Excellence.
Key Responsibilities:
Primary Role
- In collaboration with the Group Data Operations Lead, they will advise architecture enhancement and design engineering patterns for the Data Engineering Service across Azure Databricks and Azure Data Factory.
- Define and enforce the distinction between data engineering patterns (optimised for fast, large-scale processing) and integration engineering patterns (Azure Integration Services, streaming, alerting, and monitoring), ensuring work is routed to the right discipline.
- Set the optimisation strategy for the platform… caching, indexing, partitioning, and cost.
- Drive automation through the data engineering delivery lifecycle so that repeatable work is engineered out and not bloat MSP uptime.
- The role will have advanced SQL knowledge to continue delivery of pre-existing patterns and collaborate with DBA and Platform Owner from DNA CoE.
Quality, DevOps and release
- Act as the engineering quality gate: review MSP and internal code before main branch and to own the definition of done for data engineering and Integration work.
- Own the DevOps practice end to end. Focusing on CI/CD for pipelines, environment promotion, branching standards, DevOps ceremony, and release governance. DevOps represents a key skill to be deployed in this role.
- Progressively delegate review and release responsibility to the Data Engineer, with structured handover targeted across years two and three.
Governance and data management
- Establish and maintain the governance of data and algorithms used for analysis, analytical applications, and automated decision-making within the engineering estate.
- Take accountability for exploiting the value from our target systems, data delivered must be repeatable, accurate to its use case, and designed effectively for the platform.
- Collaborate with analytics engineering and data science to uphold data quality, security, and governance across the boundary between ingestion and data products.
- Support the Group Data Operations Lead in shaping the engineering service. To act as the technical SME across data engineering disciplines.
People and succession
- Mentor engineers and create promote best practice, building the succession layer beneath this role.
- Raise data literacy and engineering capability across the data operations.
Stakeholders and service
- Communicate clearly with business stakeholders who adopt processes and technology quickly who may have limited understanding of data movement.
- Context-switch across multiple concurrent lines of work while remaining accountable for each.
- Build and maintain a strong working relationship with the Centre of Excellence, through which all platform permissions and access are governed.
- Hold the MSP to the engineering standard, improving Data Engineering Service SLA’s year on year.
Experience & Skills Required:
Experience
- At least six years in data management disciplines — data integration, modelling, optimisation, and data quality — with proven delivery of modern cloud data platforms.
- Demonstrated experience leading engineering quality in a multi-supplier or managed-service environment is strongly advantageous.
- Mining, heavy industry, or comparable industrial experience is valuable.
Essential technical skills
- Expert SQL, query optimisation, indexing strategy, and performance diagnosis, not just query authorship.
- Expert DevOps capability; an advantageous skill of this role: CI/CD, release management, environment strategy, and engineering ceremony discipline.
- Proven experience extracting data from vendor and operational APIs, handling authentication schemes (OAuth2), pagination, rate limits, and schema drift.
- Fundamental excellence in Azure Data Factory, including metadata-driven and parameterised pipeline design.
- Strong development capability in Databricks and Spark, with working command of Unity Catalog, Delta Lake, and the medallion architecture.
- Strong understanding of IoT and telemetry data - high-volume, high-frequency operational data from industrial sources.
- Strong understanding of data governance and orchestration.
- Clear command of engineering versus integration patterns, and when each applies.
Desirable
- Experience with complex data types, spatial data desirable.
- Exposure to Databricks, Synapse, Power BI.
Behavioural Traits Required:
- Performance and results orientated
- Proficiency in navigating change within an evolving environment
- Capability to perform effectively in high-pressure situations
- A standard setter against their code, review discipline, and documentation become the benchmark others work to.
- Passionate about efficiency & energised by developing junior engineers.
- A strong, plain-spoken communicator, able to translate between executive, business, and technical audiences.
- Comfortable holding a supplier to account constructively and holding a line on quality.
Other Requirements:
- Travel: Internationally, minimum twice a year
- Location: London, UK
- Place of work: Hybrid (at least 3 day office requirement per week)
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