Data Modelling Engineer - Digital Factory

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Region

Australia

Location

Queensland

Work type

Full Time

Job category

Technology/IT

Date published

26 June 2020

Date closing

06 July 2020 at 00:00 GMT

About BHP

At BHP Technology we support our people to grow, learn, develop their skills and reach their potential.

With a global portfolio of operations, we offer a diverse and inclusive environment with extraordinary career opportunities. Our strategy is to focus on creating a safe work environment where our employees feel strongly connected to our values and objectives, and where the capability of our people is key to our success.

To enable the rapid deployment of digital solutions we have established Digital Teams here in Queensland. Think of us as a “startup within a company” made up of multi-disciplinary, non-hierarchical teams with one focus: delivering digital solutions that improve the lives and work of our mining teams. 

We work fast and solve tough problems, and pride ourselves on our culture of open collaboration, safety, diversity of thought and fun. We’re looking for the best and brightest developers, engineers, data scientists and designers to join us, working from our new nerve center in Brisbane.  

Come and be a part of this success.

About the role

The Data Modelling Engineer is critical in ensuring that data provided to the customer (our Assets) and used by all Digital Factory Teams – both on premise and cloud-based – is continually improving in quality, and the all data issues are identified, understood and addressed in order to achieve the high quality accurate data and make it available for key stakeholders thus enabling data driven decision making across the organization. 

The Data Modelling Engineer primary responsibility is maintaining, controlling and improving the quality of data provided from all data sources. They will be accountable for designing and modelling data in alignment with broader Asset use-cases and self-serve needs of the Digital Factory Teams and provide highly reusable and scalable data centric services that meets BHP’s data architecture strategy. 

The fun parts of this role include;
  • Understanding business problems and the Digital Factory product roadmap
  • Working with product teams to identify required data / information for their solutions and the existing sources of said data. This includes quality assessment and technical knowledge of data source capabilities (i.e., data capture frequency, known issues, etc.)
  • Working with Product Teams and the Data Integration Engineer to ensure that new data sources deployed for DF products meet data quality requirements
  • Monitors and manages metadata and ensures usefulness of metadata in improving effectiveness and quality of data
  • Work with the Data Domain Owners to understand the Data Domain, Data entities, Data Standard definitions, Data Classification rules and drive Data Quality framework to support enterprise level data architecture.
  • Implement processes and systems to monitor data quality, ensuring production data is always accurate, complete and consistent and available for key stakeholders and downstream business processes.
  • Define and govern data modeling and design standards, tools, best practices, and related development methodologies for the Data Catalog Organization
  • Create logical and physical data models using best practices to ensure high data quality and reduced redundancy
  • Develop and maintain data lineage and source to target mappings to support data platform architecture
  • Develop and maintain scalable and optimized data pipelines and build new API integrations to support continuing increases in data volume and complexity. 

About You

We are looking for people who are passionate about Data and that come from a technical background working with data in a data warehouse, data lake and/or ETL/ELT environments with hands on experience in data modelling.

Ideally you will have the following experience:
  • Experience in a Data Modeler and/or Data Engineer role
  • Have worked with simulation/ optimization and distributed computing tools
  • Expertise in data environment development and improvement across a multitude of on-premise and cloud-based data sources
  • Developing, validating, publishing and maintaining Logical/physical data models
  • Have used data modeling tools - e.g., ErWin 
  • Exposure to building data governance and quality frameworks including (data proofing, data catalog, data dictionary, business glossary and data lineage)
  • Have built and optimized big data pipelines, architectures and data sets.
  • Have worked with large datasets, relational databases (SQL), and distributed systems (Hadoop, Spark, Hive) and stream-processing systems (Spark-Streaming)
  • Object-oriented/object function scripting languages: Python, Scala, etc.
  • Data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
  • AWS cloud services: S3, EC2, EMR, RDS, Redshift and Kinesis

Supporting a diverse workforce

At BHP, we recognise that we are strengthened by diversity. We are committed to providing a work environment in which everyone is included, treated fairly and with respect. We are an Equal Opportunity employer and we encourage applications from women and Indigenous people. We know there are many aspects of our employees’ lives that are important, and work is only one of these, so we offer benefits to enable your work to fit with your life.  These benefits include flexible working options, a generous paid parental leave policy, other extended leave entitlements and parent rooms.  ImportantThe safety of BHP’s people and our candidates is our number one priority. Due to the unfolding circumstances of COVID-19 and the Government regulations in place to deal with the pandemic,   your working location and/or roster as advertised in this advert may be temporarily subject to change whilst the government regulations are in place. This is to ensure the health safety of our workforce, and to implement effective social distancing measures.  These changes if applicable will be discussed at screening and interview stage.
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