
Big Data Solutions Architect
5 days ago
We are seeking a highly skilled Big Data Solutions Architect to join our team at ACE Money Transfer. As a key member of our data architecture team, you will be responsible for designing and implementing scalable and high-performance data systems to support our business operations.
Key Responsibilities:
- Big Data Solutions: Design and implement big data solutions to process large volumes of data efficiently and effectively.
- Data Pipeline Design: Design, develop, and manage robust data pipelines and ETL processes to extract, transform, and load data from various sources into data warehouses or data lakes.
- Real-Time Data Processing: Design and implement real-time data processing pipelines using tools such as Kafka, Spark Streaming, or Flink to handle continuous data streams effectively.
Data Modeling and Schema Design: Collaborate with stakeholders to understand data requirements and design appropriate data models and database schemas that meet business needs.
Pipeline Optimization: Optimize data pipelines and ETL processes to enhance performance and efficiency, ensuring timely and accurate data delivery to end-users.
Data Quality and Reliability: Monitor, troubleshoot, and resolve issues related to data quality, consistency, and integrity to ensure the reliability and accuracy of data systems.
End-to-End Data Solutions: Architect and implement end-to-end data solutions, including data ingestion, processing, storage, and access layers, ensuring scalability and performance.
Mentorship & Team Leadership: Mentor and guide a team of data engineers, fostering a collaborative and high-performing culture. Provide thought leadership in data engineering practices and promote knowledge-sharing initiatives.
Data Governance: Implement and maintain data governance policies, ensuring compliance with data privacy and security regulations.
Cross-Functional Collaboration: Work closely with software engineers, infrastructure teams, and other cross-functional partners to optimize data infrastructure and ensure seamless integration with other systems.
Innovation and Continuous Improvement: Stay current with industry trends and technologies in data engineering, recommending innovative solutions to improve processes and systems.
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