Role Profile:

1. Working with data software engineers, product managers, Data Ops and data scientists to build data architecture that drives insights
2. Drive data cost efficiency with good data model design, minimized data replication, and cost-effective storage and usage
3. Lead the data engineering best practice inputs to data architecture (principles and adoption of MACH approaches), data security, data automation Ensure data security protocols are aligned to policy and operational
4. Ensure data architecture requirements are implemented, documented and operational

5. Designing and building reusable components, frameworks, and libraries at scale to support analytics data products

6. Improving processes regarding data flow and quality to improve data accuracy, viability, and value
7. Identifying, designing, and implementing process improvements that include building/re-engineering data models, data architectures, pipelines, and data applications
8. Continuously look for data optimization processes and oversee data management, governance, security, and analysis
9. Increase automation and build analytic solutions at scale to serve the business requirement

The below Key Performance Areas include but are not limited to:

1. Data engineering: ETL/ELT design and build for data batching and streaming; data standardisation and reference data management; able to leverage data automation methods to provide data-as-a-service; deliver data lineage operation and optimization
2. Hands-on experience analyzing, architecting/re-architecting data and analytics platforms on AWS/GCP or similar platform
3. Test-driven development: able to build pipelines, data transformations and business rules for data management using Python and/or Spark. Able to identify, recruit, train and retain the necessary team to manage data engineering work at Alshaya
4. Experience in designing and implementing data ingestion solutions on AWS or GCP using different approaches
5. Must have experience designing and architecting data lakes on cloud platforms and BI application integrations.
6. Knowledge of data security best practices and experience building secure data solutions
7. Experience designing and setting up a lakehouse architecture using Databricks or any similar technologies  
8. Knowledge of data visualizations and self-service data preparation tools to maintain the flow of data for the analytics and visualization teams
9. Collab/management: Lead a team of Data Engineers, Devops, Business Analysts
10. Experience managing engineers and guiding them through project planning, design, development, quality control, and deployment

Mentorship and career growth where you will be responsible for 121’s, Goal setting, career development, code reviews

Skills: Hands on experience in writing Python ETL and orchestration of ETL jobs

- proficient in SQL

- Infra as a code using Terraform or similar

- hands on experience in tools liked Cloudwatch/Grafana or similar for Monitoring and Alerting

- Experience working in an AGILE environment

- Experience with CICD pipeline and DataOps methods; managing data model deployments and tuning

- Code repository management, containerization experience

- Experience with Data Security Frameworks