Data Pipeline Development: Design, implement, and maintain ETL processes to extract, transform, and load data from diverse sources into data storage systems.
Data Integration: Integrate data from various structured and unstructured sources, including databases, APIs, logs, and external data feeds.
Data Modeling: Develop and maintain data models that align with business requirements, ensuring data is stored efficiently and accurately.
Data Quality Assurance: Implement data quality checks and validation procedures to ensure data accuracy, consistency, and completeness.
Data Warehousing: Build and maintain data warehouses or data lakes, optimizing them for query performance and scalability.
Documentation: Create and maintain documentation for data pipelines, data models, and infrastructure configurations.
Collaboration: Collaborate with analysts, and other stakeholders to understand data requirements and provide data support for analytics and reporting.
Monitoring and Troubleshooting: Set up monitoring systems to proactively identify issues and troubleshoot data pipeline failures or performance bottlenecks.
Qualifications:
Bachelor's or higher degree in computer science engineering, information technology, or a related field.
Strong programming skills Python and associated libraries: Pandas, SQL Alchemy, Flask, Fast API etc.
Proficiency in SQL and experience with relational and NoSQL databases.
Knowledge of data warehousing technologies like Amazon Redshift, Google BigQuery, Snowflake, or similar is a Plus
Experience with data pipeline orchestration tools like Apache Airflow, Luigi, or similar.
Familiarity with cloud platform AWS is a plus.
Understanding of data integration techniques, data modeling, and data architecture best practices.
Excellent problem-solving and troubleshooting skills.
Strong communication and collaboration skills.
Knowledge of data security and compliance standards is a plus.
Experience with big data technologies (e.g., Hadoop, Spark) is a bonus
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