Full-Time Data Analytics & Insights Data Engineer

The DAI Data Engineer [DE – Backend] is a direct report to the head of Data Analytics & Insights (DAI). The DE will focus on overseeing the full lifecycle of back end development, implementation and deployment of the Operational Data Hub, Data Marts, Data Lake and DAI Reporting environments, and the support of the integration of those systems with other related applications.

The DE works closely with the Data Architect, Enterprise Solution Architect(s), functional analysts, data analysts/owners, data scientists, and data engineering teams in order to power insight and avail meaningful data products for the business and enable consistently informed management decisions.

The DE understands how data is turned into information and knowledge and how the knowledge supports and enables key business processes within the organization. The DE also has a thorough understanding of the enabling Information Technology, DWH.

Key Responsibilities and Duties:

1. Designs, develops, implements and maintains data pipelines to ensure efficient data processing and storage.
2. Ensures data quality and integrity by implementing data validation and cleansing processes.
3. Develops and maintains ETL/ELT processes to migrate and deploy data across systems.
4. Collaborates with cross-functional teams to understand data requirements and design appropriate solutions.
5. Works with other teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies
6. Performs unit tests and conducts reviews with other team members to make sure code is rigorously designed, elegantly coded, and effectively tuned for performance.
7. Support problem solving.

Key Accountabilities:

1. Project/Work Planning: Participates in work stream planning process including initiation, technical design, development and prototyping, testing, and delivery of BI/Analytical solutions. May participate in project management estimation process. May participate in the development of business cases to support BI/Analytical projects.
2. Business/BI Requirements: Works with Stakeholders, Business Analysts, Data Architect, Data Engineers, Data Integration Specialists to understand customer needs. Analyzes business and functional requirements and translates these requirements into robust, scalable, operable data solutions/frameworks.
3. Development and Implementation/Deployment: Defines, promotes and implements the department’s best practices and design principles for data warehousing and/or Data Lake techniques and architecture. Designs, implements and supports the business’s database and table schemas for new and existent data sources for the data warehouse. Creates and supports the ETL/ELT in order to facilitate the accommodation of data into the warehouse and or data Lake using e.g. SSIS and/or other technologies. In this capacity, the DE designs, develops and implements systems for the maintenance of the business’s Data Lake and/or data warehouse, ETL processes, and business intelligence. Documents new and existing models, solutions, and implementations for all BI processes.
4. Production Support: Provides day-to-day support of the data warehouse and data lake and troubleshoots existing procedures and processes. Strives to improve data organization and accuracy: monitoring and troubleshooting performance issues on data warehouse servers and assisting in the development of business intelligence, business data standards, and processes. Support Database Administrators and Developers to build data warehousing systems, data lakes for business intelligence, reporting and analytics. Supports and helps manage external resources, such as service providers and vendor support.
5. Quality Assurance: Ensures the collected data is within required quality standards [Accuracy, Completeness, Consistency, Uniqueness, Timeliness, Relevance and Validity]. Maintains the quality of Metadata Repository by adding, modifying, and deleting data.
6. Processes, Policies & Standards: Creates scalable, efficient, automated processes for large scale data analyses, model development, model validation, and model implementation. Documents standards and policies for the form, structure, and attributes of the BI platform, tools and systems. Ensures adherence to processes, policies, and standards.
7. Research and evaluation: Gathering and maintaining best practices that can be adopted in big data stacking and sharing across the business.
8. Coaching/Mentoring: Provides guidance, training, and problem solving assistance to other team members. Provides coaching to less-experienced individuals. Provides expertise to the business in the areas of data analysis, reporting, data warehousing, and business intelligence. Provides technical expertise to the business on business intelligence data architecture and also on structured approaches for transitioning manual applications and reports to the business.

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