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Unlocking Business Value through Expert Data Engineering
3 weeks ago
As a data engineer, you will play a key role in handling large datasets and cloud architectures to support analytics and AI solutions. This involves working with diverse datasets, validating and standardizing data, building pipelines for ingesting and loading data, and optimizing storage for efficient processing.
Main Responsibilities:- Create and maintain high-quality data assets by ensuring consistency, accuracy, and reliability.
- Become proficient in data pipeline development using ETL/ELT workflows (e.g., PySpark, Airflow) and automation tools like CI/CD (e.g., GitLab CI/CD, Jenkins).
- Design effective data models and schemas that meet business requirements, leveraging techniques such as normalization, denormalization, star/snowflake schemas, or data vaults.
- Drive efficiency in data processing through query optimization and storage tuning.
- Integrate and enrich data by mapping infrastructure metadata to geospatial context, augmenting metrics, and creating unified time-series datasets.
- Degree in Computer Science, Software Engineering, Data Science, or equivalent experience.
- 2+ years of experience in data engineering, analytics, or related roles.
- Proficiency in Python for ETL/data engineering and Spark (PySpark) for large-scale pipelines.
- Experience with Big Data frameworks and SQL engines (e.g., Spark SQL, Redshift, PostgreSQL) for data marts and analytics.
This is an excellent opportunity for a skilled data professional to work on innovative data-driven projects, collaborating with cross-functional teams and driving technical excellence.
