Key Responsibilities:

Design and implement a scalable and efficient data lake architecture to support our growing data needs.
Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs.
Develop and maintain data lake infrastructure, including data ingestion, storage, and processing systems.
Ensure data quality and consistency across the data lake, implementing data governance and data quality processes.
Monitor and troubleshoot data lake performance issues and recommend solutions.
Provide technical leadership to the data engineering team, guiding them in the implementation of data lake solutions.
Stay up-to-date with industry trends and best practices in data lake architecture, and recommend improvements and optimizations to our existing system.
Collaborate with data scientists, analysts, and other stakeholders to understand data usage and make recommendations for data lake improvements.
Communicate complex technical concepts to non-technical stakeholders, including senior management, to drive understanding and support for data lake initiatives.

Qualifications:

Bachelor's or Master's degree in Computer Science, Information Systems, or a related field.
Minimum of 8 years of experience in data engineering, data architecture, or a related role.
Extensive experience designing and implementing data lake architecture using tools such as Hadoop, Spark, Kafka, and NoSQL databases.
Strong understanding of data warehousing and ETL processes.
Experience with cloud-based data lake solutions, preferably AWS.
Excellent problem-solving and analytical skills.
Proven track record of delivering large-scale data lake projects on time and within budget.
Strong communication and interpersonal skills, with the ability to work effectively with cross-functional teams.
Experience with Agile methodologies is a plus.
Data engineering certifications are a plus.

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