Manager | Data Engineering |

Key Responsibilities:

• Lead, mentor, and manage a team of data engineers, providing guidance, training, and fostering an environment of continuous improvement.
• Design, develop, and maintain robust data pipelines and data infrastructure to support business intelligence, data analytics, and machine learning initiatives.
• Collaborate with cross-functional teams (e.g., Data Science, Product, IT) to define data requirements, ensure data availability, and optimize data flows.
• Oversee the architecture and optimization of large-scale data storage solutions (e.g., data lakes, data warehouses) in cloud platforms such as AWS, Azure, or Google Cloud.
• Manage the creation of automated processes for data ingestion, transformation, and storage, ensuring data integrity, quality, and performance.
• Lead efforts to improve data security, governance, and compliance practices for all data assets.
• Stay current with emerging technologies and trends in data engineering, making recommendations for the adoption of new tools and technologies.
• Drive the evolution of the data engineering strategy, aligning with business objectives and technical requirements.
• Manage the day-to-day operations of the data engineering team, ensuring timely delivery of data solutions and projects.
• Design and implement monitoring, logging, and alerting systems to ensure data pipelines and infrastructure are running efficiently.
• Collaborate with the data architecture team to design and implement scalable data architectures that support the organization's long-term goals.
• Lead performance tuning and optimization of data systems to improve the speed, reliability, and scalability of data processing.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related field.
• Minimum of [X] years of experience in data engineering, with at least [Y] years in a managerial or leadership role.
• Strong experience with cloud platforms such as AWS, Google Cloud, or Microsoft Azure, and their data engineering services (e.g., S3, Redshift, BigQuery, etc.).
• Proficiency in data processing technologies like Apache Spark, Hadoop, Kafka, or other ETL tools.
• Expertise in programming languages such as Python, Java, or Scala, with experience in building and maintaining data pipelines.
• Experience with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB).
• Knowledge of data warehousing, data lakes, and ETL processes.
• Familiarity with data modeling, data governance, and data quality practices.
• Experience with containerization and orchestration tools such as Docker and Kubernetes is a plus.
• Proven experience managing and scaling data engineering teams in a fast-paced environment.
• Strong problem-solving and analytical skills, with a track record of delivering innovative data solutions.

Skills and Competencies:

• Leadership: Ability to manage and inspire a high-performing data engineering team.
• Communication: Excellent communication skills, both written and verbal, with the ability to interact with stakeholders across technical and non-technical teams.
• Collaboration: Strong interpersonal skills with a collaborative mindset, working closely with other teams such as data science, business intelligence, and software engineering.
• Project Management: Experience managing multiple data engineering projects simultaneously, with a focus on delivery, quality, and timelines.
• Problem-Solving: Strong troubleshooting skills with the ability to resolve complex data-related challenges.
• Business Acumen: Ability to understand business needs and translate them into technical solutions that provide measurable business value.

Preferred Experience:

• Experience with machine learning workflows or data science pipelines.
• Knowledge of streaming data platforms (e.g., Apache Flink, Apache Storm).
• Experience with CI/CD pipelines and automated testing for data engineering projects.
• Familiarity with data visualization tools (e.g., Tableau, Power BI) or experience working with business intelligence teams.
• Previous experience in the Telecommunications or Technology (T&T) sector is a plus.