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Data Engineer
Ellisdon
A leading construction and building services firm specializing in innovative solutions.
Develop and maintain highly scalable data management systems, design ETL processes, and provide thought leadership for data solutions
Design, construct, install, test, and maintain highly scalable data management systems while integrating new data management technologies into existing structures.
Leverage the latest artificial intelligence tools and platforms to automate data validation, anomaly detection, and predictive analytics, advancing the efficiency and intelligence of data engineering workflows.
Be a technical lead in the development of high-volume platforms and data pipelines.
Develop ETL processes to move internal and external data into EllisDon's cloud-based data lake and data warehouse(s).
Provide thought leadership for all data solutions, including designing data solutions that meet and exceed customer expectations.
Leverage artificial intelligence and machine learning techniques to enhance data processing, optimize workflows, and drive actionable insights from complex datasets.
Work with relevant stakeholders to identify sources of data and map out processes/data flow while researching opportunities for data acquisition and innovative new uses for existing data.
Continually improve our data pipelines and find innovative ways to maximize automation while recommending ways to improve data reliability, efficiency, and quality.
Provide input and feedback to support continuous improvement in team processes.
What you bring
experience
sql
python
azure
mongodb
degree
Comfortable working in a fast-paced and collaborative environment.
Have previous experience in data engineering, data warehousing, data modeling, or data analysis.
Have excellent communication, analytical, and problem-solving skills.
Have a degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
Demonstrate expertise and proficiency in programming languages and relevant toolsets, including Python, SQL, ETL methodologies and associated platforms, the Azure and GCP ecosystems, database design principles, MS SQL, MySQL, Synapse, Big Query, NoSQL databases such as MongoDB, Airflow, and real-time data streaming technologies.
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