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.
