Azure Data Engineering Architect

Tasks

Collaborate with stakeholders to understand business requirements and translate them into data engineering solutions.

Design and oversee the overall data architecture and infrastructure, ensuring scalability, performance, security, maintainability, and adherence to industry best practices.

Define data models and data schemas to meet business needs, considering factors such as data volume, velocity, variety, and veracity.

Select and integrate appropriate data technologies and tools, such as databases, data lakes, data warehouses, and big data frameworks, to support data processing and analysis.

Create scalable and efficient data processing frameworks, including ETL (Extract, Transform, Load) processes, data pipelines, and data integration solutions.

Ensure that data engineering solutions align with the organization's long-term data strategy and goals.

Evaluate and recommend data governance strategies and practices, including data privacy, security, and compliance measures.

Collaborate with data scientists, analysts, and other stakeholders to define data requirements and enable effective data analysis and reporting.

Provide technical guidance and expertise to data engineering teams, promoting best practices and ensuring high-quality deliverables. Support to team throughout the implementation process, answering questions and addressing issues as they arise.

Oversee the implementation of the solution, ensuring that it is implemented according to the design documents and technical specifications.

Stay updated with emerging trends and technologies in data engineering, recommending and implementing innovative solutions as appropriate.

Conduct performance analysis and optimization of data engineering systems, identifying and resolving bottlenecks and inefficiencies.

Ensure data quality and integrity throughout the data engineering processes, implementing appropriate validation and monitoring mechanisms.

Collaborate with cross-functional teams to integrate data engineering solutions with other systems and applications.

Participate in project planning and estimation, providing technical insights and recommendations.

Document data architecture, infrastructure, and design decisions, ensuring clear and up-to-date documentation for implementation, reference and knowledge sharing.

Requirements

Proven work experience as a Data Engineering Architect or a similar role and strong experience in in the Data & Analytics area.

Strong understanding of data engineering concepts, including data modeling, ETL processes, data pipelines, and data governance.

Expertise in designing and implementing scalable and efficient data processing frameworks.

In-depth knowledge of various data technologies and tools, such as relational databases, NoSQL databases, data lakes, data warehouses, and big data frameworks (e.g., Hadoop, Spark).

Experience in selecting and integrating appropriate technologies to meet business requirements and long-term data strategy.

Ability to work closely with stakeholders to understand business needs and translate them into data engineering solutions.

Strong analytical and problem-solving skills, with the ability to identify and address complex data engineering challenges.

Proficiency in Python, PySpark, SQL.

Familiarity with cloud platforms and services, such as AWS, GCP, or Azure, and experience in designing and implementing data solutions in a cloud environment.

Knowledge of data governance principles and best practices, including data privacy and security regulations.

Excellent communication and collaboration skills, with the ability to effectively communicate technical concepts to non-technical stakeholders.

Experience in leading and mentoring data engineering teams, providing guidance and technical expertise.

Familiarity with agile methodologies and experience in working in agile development environments.

Continuous learning mindset, staying updated with the latest advancements and trends in data engineering and related technologies.

Strong project management skills, with the ability to prioritize tasks, manage timelines, and deliver high-quality results within designated deadlines.

Strong understanding of distributed computing principles, including parallel processing, data partitioning, and fault-tolerance.

Bachelor's degree in Computer Science, Information Technology, or a related field. A Master's degree may be preferred.

Missing one or two of these qualifications? We still want to hear from you! If you bring a positive mindset, we'll provide an environment where you feel valued and empowered to learn and grow.