Job Responsibilities
We are looking for creative thinkers and problem-solvers to design new and innovative solutions using the latest technologies available.
Your primary responsibilities would include understanding the business requirements and providing structured data using distributed computing in the cloud to address those requirements.
In collaboration with the rest of the team, you would be responsible for building and maintaining high-quality data pipelines that would service the needs of people around the world!
Job Description:
- Designs and builds end ‑ to ‑ end data pipelines (ingestion transformation storage);
- Ensures data reliability, accuracy, and timeliness;
- Structures raw data into analytics ‑ ready datasets;
- Builds and maintains data warehouses, data lakes, and semantic layers;
- Monitors data pipelines and prevents/diagnoses data issues;
- Enable the timely availability of good-quality data to facilitate data-oriented management decisions;
- Work with new data sources from around the world to provide data to our Global Data Analytics Platform built on Azure Cloud;
- Implement automated Data Quality measures to sieve out bad quality data for timely rectifications by data sources;
- Design, construct, and maintain Data Warehouse models to support PSA regional data analytics needs;
- Create and maintain data pipelines to transform and load data from data lake into Data Warehouse models;
- Work and collaborate with PSA data analytics team at all stages of CRISP-DM Methodology;
- Align to PSA data analytics policy, standards, and best practices and document as required;
- Degree from a recognized university, preferably in Data Analytics or Computer Science domain.
Requirements
Required Technical Skills:
- At least 4 years of hands-on experience as a Data Engineer, including at least two years using Microsoft data tools;
- Must have knowledge of data warehouse concepts (Star, snowflake) methodology and experience in designing data warehouse dimensional modelling;
- Experience in building enterprise models, tabular cubes, and perspectives;
- Ability to assess the performance of models and introduce optimizations;
- Experience in data processing with Python is a must;
- Good knowledge of SQL is a must;
- Good understanding and/or experience with big data.
Advantages:
- Experience with writing data applications in Kubernetes;
- Experience with REST APIs;
- Knowledge of cloud computing/distributed computing;
- Experience with Cloud Data storage services (in particular Azure Queue Storage/Azure Blob Storage);
- Hands-on experience in Azure Data Factory, Azure Synapse Analytics / Data Warehouse, Azure Analysis Services; Azure Databricks