– Design and implement scalable data pipelines for efficient collection, processing, and analysis of large datasets from multiple sources.
– Collaborate with data scientists to transition machine learning models into production-ready applications.
– Optimize and maintain data workflows, ensuring accuracy, consistency, and integrity throughout the process.
– Evaluate and integrate new data management technologies to enhance analytics capabilities.
– Develop and manage data repositories, following best practices for governance and security.
– Create comprehensive documentation and provide training to team members on data systems and workflows.
– Leverage cloud platforms such as AWS, Azure, and Google Cloud for data storage, processing, and machine learning deployment.
– Stay up to date with industry trends and emerging technologies in data engineering and machine learning.
– Lead the development and deployment of machine learning models, driving key business outcomes and performance metrics.
– Build and maintain scalable data pipelines and feature stores for training and inference use cases.
– Collaborate with product, engineering, and business teams to identify high-impact data science opportunities and ensure seamless execution.
– Monitor and retrain production models to maintain performance and address data drift challenges.
– Contribute to architectural decisions for data platforms and model-serving infrastructure.
– Mentor junior team members and promote best practices in data science engineering.

Qualifications:

– Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related technical discipline.
– 3–5 years of experience in data science and engineering, with a proven track record of delivering production-ready solutions.
– Strong ability to translate business problems into data science initiatives with measurable outcomes.
– Extensive hands-on experience in some of the following technologies, Databricks, SQL, Python, and Spark for large-scale data processing.
– Strong expertise in data warehousing technologies (e.g., Snowflake, Redshift, BigQuery, or similar).
– Experience with distributed data processing frameworks (Spark, Hadoop, or similar).
– Deep understanding of ETL processes, data modeling, and schema design.
– Strong experience with cloud-based data solutions (AWS, Azure, GCP) and data lake architectures.
– Experience with automation, CI/CD for data pipelines, and Infrastructure as Code (IaC).

Note:

— Please share relevant CVs at resume@thezedconsulting.com and mention the job title in the subject.