Azure Data Engineering Architect - freelance

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.