Job
Description
Role Overview: As a skilled and motivated Senior Analytical Data Engineer at Springer Nature, you will drive the development of scalable and efficient data solutions. Your crucial role will involve designing and building ETL/ELT pipelines, optimizing data architectures, and enabling business insights through effective data models. This opportunity allows you to collaborate with cross-functional teams on high-impact projects aligning data engineering practices with business goals. Key Responsibilities: - Design, implement, test, and maintain scalable and efficient ETL/ELT pipelines for ingesting and transforming large datasets from diverse sources. - Architect, build, test, and optimize data warehouses and data lakes, focusing on data consistency, performance, and analytics best practices. - Collaborate with data science, analytics, and business intelligence teams to understand data needs, translate requirements into data models, and develop reliable data sources. - Implement data quality checks, validation rules, and monitoring to ensure data accuracy, integrity, and security, contributing to data governance initiatives. - Continuously monitor and enhance database and query performance, identifying opportunities to streamline and improve data retrieval times. - Define and design frameworks for monitoring data quality and data pipelines, including monitoring Google BigQuery consumption. - Evaluate, select, and integrate new tools and technologies to enhance data capabilities, automate manual processes, and improve efficiency and accuracy. - Mentor junior engineers, share best practices in data engineering and analysis, and help build a culture of technical excellence within the team. - Adhere to development guidelines and quality standards. Qualifications Required: - Over 6 years of experience in data engineering with strong technical skills in SQL, Python, Google Cloud Platform (GCP), Google BigQuery, data pipeline tools (e.g., Apache Airflow, DBT), infrastructure tools like Terraform, and data modeling concepts. - Proficiency in working with both NoSQL and SQL databases, designing scalable ETL/ELT pipelines, and optimizing data processing workflows. - Strong analytical skills with the ability to translate business needs into actionable data solutions, familiarity with data visualization tools like Looker, and experience in communicating complex data findings to technical and non-technical stakeholders effectively. - Soft skills including strong problem-solving abilities, the capacity to work in a fast-paced environment, proven collaboration with various stakeholders, and mentoring experience within the data engineering field. Company Details: Springer Nature is a leading global research, educational, and professional publisher with a diverse and inclusive culture that values the contributions of its teams. The company empowers colleagues from various backgrounds and perspectives to attract, nurture, and develop top talent. Springer Nature was recognized as the Diversity Team of the Year at the 2022 British Diversity Awards, showcasing its commitment to diversity, equity, and inclusion. Role Overview: As a skilled and motivated Senior Analytical Data Engineer at Springer Nature, you will drive the development of scalable and efficient data solutions. Your crucial role will involve designing and building ETL/ELT pipelines, optimizing data architectures, and enabling business insights through effective data models. This opportunity allows you to collaborate with cross-functional teams on high-impact projects aligning data engineering practices with business goals. Key Responsibilities: - Design, implement, test, and maintain scalable and efficient ETL/ELT pipelines for ingesting and transforming large datasets from diverse sources. - Architect, build, test, and optimize data warehouses and data lakes, focusing on data consistency, performance, and analytics best practices. - Collaborate with data science, analytics, and business intelligence teams to understand data needs, translate requirements into data models, and develop reliable data sources. - Implement data quality checks, validation rules, and monitoring to ensure data accuracy, integrity, and security, contributing to data governance initiatives. - Continuously monitor and enhance database and query performance, identifying opportunities to streamline and improve data retrieval times. - Define and design frameworks for monitoring data quality and data pipelines, including monitoring Google BigQuery consumption. - Evaluate, select, and integrate new tools and technologies to enhance data capabilities, automate manual processes, and improve efficiency and accuracy. - Mentor junior engineers, share best practices in data engineering and analysis, and help build a culture of technical excellence within the team. - Adhere to development guidelines and quality standards. Qualifications Required: - Over 6 years of experience in data engineering with st
