The Data Engineer will build out the wavemoney data pipeline & infastructure. We are at the beginning of an exciting journey without data infastructure and this a greenfield opportunity. In the first six months this role will develop our transactional data warehouse on our microsoft platform. Over the coming tweleve months we will build a fully intergrated cloud based data infastructure which will join our digital and social media data to our transactional data.
The Data Engineer is responsible for the development, maintenance, improvement, cleaning, and manipulation of data in the business’s operational and analytics data warehouse.
The Data Engineer works with data analytics teams, data scientists, and other data warehouse engineers in order to understand and aid in the implementation of enterprise data warehouse / enterprise data lake requirements, analyze performance, and troubleshoot any existing issues.
The Data Engineer has to be an expert in ETL technologies, SQL development, and Shell Scripting, further spearheading the data and analytics database design (Logical Models & Physical Data Models), creation of master data and maintaining data flow activities.
We are going to make Wave Money a data driven organisation and this role is instumental in making this happen.
Key Result Areas
The Data Engineer designs and creates enterprise data warehouse systems optimized for performance, implementing schema changes, and maintaining data architecture standards across all of the business’s functions.
The Data Engineer is additionally tasked with designing and developing scalable ETL packages from the business source systems and the development of ETL routines/jobs in order to populate databases from sources and also to create FACTs, Aggregates & Dimensions.
In this capacity, the Data Engineer is also responsible for enabling and running data migrations across different databases and different servers, for example, data migration from MySQL/Oracle to SQL servers. S/He defines and implements data stores based on system requirements and consumer requirements.
The Data Engineer strives to ensure proper data governance and quality across the Data and Analytics department and the business as a whole.
The data engineer works collaboratively with the entire Data and Analytics team, providing support to the entire department for its data centric needs.
Competencies and Behaviours
- Technical : Keeps upto date with latest trends in data and data engineering (Both on Premises & Cloud Practices). Passion for data : Belief that data and insight can grow and transformation an organization.
- Partnership : Successful professional approaches to collaboration with all stakeholders and team;
- Communication: Creating and promoting enabling environment for open communication; Constructively challenge those with power and authority
- Governance : Work well with the Management, regardless of its composition; contribute to Management; Adhere to clear lines of responsibility and accountability
- Management: Create a positive and productive work environment, Model proper staff behavior and promote inclusive practices; create a sense of shared responsibility/credit for accomplishments and shared responsibility for challenges or failures; Lead an efficient and effective organizational operation according to best practices.
- Decision-making : Delegate appropriate decisions and responsibilities; Make clear and timely decisions; Fair and transparent decision making
- Organizational Development: Create a work environment in which learning is; creating an emotionally intelligent organization, staff competence; Building and promoting effective teams, creating an environment of creativity and innovation.
Experience, Functional Skills and Knowledge Areas
- 5+ Year experience of data engineering
- Experience with high data volumes, both digital and transactional.
- Experience of building and planning a new data infrastructure for a sizable organization.
- Must have hands on experience on full project development lifecycle related to Data Warehouse & Engineering.
- Understanding of Agile development will be an added advantage.
Education and qualifications:
- University degree in a quantitative subject.
- Experience in the usage of computers and office software packages (MS Word, Excel, PowerPoint, etc.)
Requirement: Fluent in English and knowledge of local language (Myanmar) is an asset
Technology and Methodology
- Knowledge of data warehousing principles. (Data Wrangling, Data Profiling, Integration, etc.)
- Strong database skills like relational database, Advanced SQL and NoSQL database.
- Data modelling skills (Entity Relationships, Logical Data Model, Physical Data Model, Dimensional Modeling, etc.)
- Expert level hands-on experience in ETL and Reporting technologies like SSIS, SSRS, SSAS and Power BI 3 dimensional cube design.
- Good understanding on SQL Server Administration & Management.
- Strong understanding and hands on experience on MySQL databases, Microsoft SQL and Oracle.
- Ability to develop & update detailed technical documentation.
- Awareness ofIoT platforms/devices used in FinTech and Digital Money/Payments