Website DHL
Position Summary:
As a leader in DHL eCommerce Data Architecture topics, you will provide best-fit architectural solutions to manage our global data ecosystem. You will work with key stakeholders, business partners, IT counterparts and the Enterprise Architect function to define requirements, identify source data, assist with detailed data models, analyze and validate data, troubleshoot data issues, and promote data quality. You will design, develop and support data services that ensure the fulfillment of business information needs and support our digitalization journey.
The primary focus of the Big Data Architect is to improve the consistency, timeliness, quality, security and delivery of data while removing unnecessary costs from the complex, fragmented, heterogeneous data architecture.
This role does start as an individual contributor and you must have strong hands-on skills in all the areas, the team will increase as resources are needed.
Key Responsibilities:
- Mapping data sources: An understanding of where data is stored and maintained. Although the application teams are responsible for documenting the details of application data, you must know where data is maintained and accessed.
- Documenting existing data movement: record how data movement is happening. You will coordinate efforts and foster consistency while documenting the frequency of movement, the source and destination of each step, how the data is transformed as it moves, and any aggregation or calculations.
- Defining integrative views of data: These views will draw together data from across the domains.
- Some views will use a database of extracted data – others will bring together data in “near real time.” You will work with business people and data engineers and application designers to identify and model these integrative views and determine the quality of service requirements — data currency, availability, response times and data volumes.
- Design and implement redundant systems, policies, and procedures for disaster recovery and data archiving to ensure effective availability, protection, and integrity of data assets.
- Keep current on big data and data visualization technology trends, evaluate, work on proof-of-concept and make recommendations on the technologies based on their merit.
- Develop a technical center of excellence within the analytics organization through training, mentorship, and process innovation.
- Designing new data movements: Having documented the status quo – the sources of data and how the data is moved around – you will then look at how this movement can be improved.
- Act as a subject matter expert for technical guidance, solution design and best practices within the Business Analytics organization.
- Create the Master Data Management strategy as well as the data governance and data security.
Required Education & Experience:
- Overview on architecture trends with an eye on market/technical conditions and future direction.
- Have a solid understanding of delivery methodology (SCRUM, Waterfall) and lead teams in the implementation of the solution according to the design/architecture.
- 5 to 8 years of designing Big Data Pipelines supporting Data Science Use Cases.
- 10+ years of experience of IT platform implementation in a highly technical and analytical role.
- Experience in project and solution estimation and team structure definition.
- 5 to 8 years of professional experience with Data Management technologies and a minimum of 3 years of Hadoop, NoSQL, and Open-Source data management technologies.
- Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics.
- Experience with structured and unstructured data.
- 3 to 5 years of Hadoop/NoSQL project implementations that exploit the full capabilities (discover, design, implement and optimize) is necessary.
- Experience in defining new architectures and ability to drive an independent project from an architectural standpoint.
- Excellent Python/Scala and Spark skills.
- Hands-on experience with data architecting, data mining, large-scale data modeling, and business requirements gathering/analysis.
- Expertise in ETL/ELT Architecture and process designing, experience of implementation and performance tuning of mappings/jobs/transformations.
- Predictive analytics: R, Azure Machine Learning.
- Leadership and team management experience with ability to provide strategic planning and oversight.