1. Building a High-Performing Data Platform:
Assist in building a data platform to support reporting tools and analytics.
Focus on optimizing data models for performance.
2. Data Tools Development:
Create data tools for analytics and optimize data models.
Evaluate and communicate the pros and cons of different modelling approaches.
3. Production Data Pipelines:
Design and build production data pipelines within a big data architecture.
Utilize continuous delivery practices for deploying, supporting, and operating data pipelines.
4. Understanding of Data Enterprise Applications:
Gain a thorough understanding of data enterprise applications such as unified ID, de-duplication, classification, and aggregation models.
Identify and prioritize the source of truth for data.
5. Data Quality Integration:
Integrate data quality practices into day-to-day work and the delivery process.
Ensure data quality is maintained throughout the data lifecycle.
6. Database Experience:
Work with relational SQL databases and NoSQL databases such as Redshift or cloud-based OLAP databases like Snowflake.
Write and process jobs to ingest structured and unstructured data from various sources and formats, including Rest APIs, flat files, logs, SQL, and NoSQL databases
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