Principal Data Engineer

Our prestigious client in Dubai Internet City is hiring principal data engineer.

Responsibilities:

1. Designing Data Architecture: Developing and architecting data systems and infrastructure to support the organization's data needs. This includes designing data pipelines, data warehouses, and data lakes.
2. Data Pipeline Development: Building and optimizing data pipelines for ingesting, processing, and transforming large volumes of data from various sources into usable formats for analysis and reporting.
3. Data Integration: Integrating disparate data sources and systems to ensure data consistency, accuracy, and reliability across the organization.
4. Performance Optimization: Optimizing data processing and query performance to ensure timely access to data for analytics and reporting purposes.
5. Data Governance and Security: Implementing data governance policies and procedures to ensure data quality, integrity, and security. This includes defining data access controls, encryption, and compliance with regulatory requirements.
6. Team Leadership: Providing technical leadership and guidance to a team of data engineers. This may involve mentoring junior team members, conducting code reviews, and driving best practices in data engineering.
7. Collaboration: Collaborating with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand data requirements and deliver solutions that meet business objectives.

Qualifications:

1. Strong Technical Skills: Proficiency in programming languages commonly used in data engineering, such as Python, Java, or Scala. Experience with data processing frameworks like Apache Hadoop, Apache Spark, or Apache Flink is also essential.
2. Data Modeling and Database Skills: Expertise in relational and NoSQL databases, as well as data modeling techniques. Experience with database technologies like PostgreSQL, MySQL, MongoDB, or Cassandra is often required.
3. Big Data Technologies: Knowledge of big data technologies and platforms, such as Apache Hadoop ecosystem (HDFS, MapReduce, Hive, HBase), Apache Spark, and cloud-based data platforms like AWS, Azure, or Google Cloud Platform.
4. ETL and Data Warehousing: Experience with Extract, Transform, Load (ETL) processes and tools, as well as data warehousing concepts and methodologies. Familiarity with tools like Apache Airflow, Talend, or Informatica is beneficial.
5. Problem-Solving Skills: Strong analytical and problem-solving skills, with the ability to troubleshoot complex data issues and devise effective solutions.
6. Communication and Leadership: Excellent communication and interpersonal skills, with the ability to communicate technical concepts to non-technical stakeholders. Experience leading and mentoring a team of data engineers is often required.