Head of Data Engineering

Position Overview

The Head of Data Engineering is a senior leadership role responsible for defining, building, and managing the organizationâ€s data infrastructure and engineering capabilities. The individual will lead a team of data engineers, architects, and analysts to design scalable data solutions, ensure efficient data pipelines, and enable advanced analytics for strategic decision-making.

Key Responsibilities

Leadership & Strategy

1. Develop and execute the organizationâ€s data engineering strategy in alignment with business goals.

2. Provide technical leadership and mentorship to the data engineering team.

3. Collaborate with cross-functional teams, including analytics, product, and IT, to understand data needs and priorities.

4. Stay updated with emerging trends and technologies in data engineering and recommend adoption where applicable.

Data Infrastructure & Architecture

1. Design and implement scalable, secure, and robust data architectures to support enterprise-wide data needs.

2. Oversee the development and maintenance of ETL/ELT processes, data warehouses, and data lakes.

3. Ensure efficient data storage, integration, and retrieval systems that support both real-time and batch processing.

4. Define and enforce standards for data governance, quality, and security.

Data Pipeline Management

1. Build and manage high-performance data pipelines that process large volumes of structured and unstructured data.

2. Optimize existing pipelines and troubleshoot issues related to data processing.

3. Enable seamless data integration from various sources such as APIs, third-party platforms, and internal systems.

Team Management

1. Hire, train, and manage a team of skilled data engineers and related roles.

2. Foster a culture of innovation, collaboration, and continuous improvement within the team.

3. Establish clear objectives, KPIs, and performance metrics for the team.

Governance, Compliance & Risk Management

1. Ensure adherence to data privacy regulations (e.g., GDPR, PDPA, CCPA).

2. Mitigate risks related to data breaches or loss through strong security practices.

3. Collaborate with legal and compliance teams to address regulatory requirements.

Performance Monitoring & Optimization

1. Monitor the performance of data systems and infrastructure, ensuring uptime and reliability.

2. Optimize data processing costs while maintaining performance and scalability.

Qualifications

1. Bachelor or Master's degree in Computer Science, Data Engineering, or related field.

2. 8+ years of experience in data engineering, with 3+ years in a leadership role.

3. Strong expertise in cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Hadoop, Spark, Kafka).

4. Proficiency in programming languages such as Python, Java, or Scala, and database systems (SQL and NoSQL).

5. Experience with data pipeline tools (e.g., Airflow, DBT) and version control systems (e.g., Git).

6. Strong knowledge of data governance, data quality, and security best practices.

7. Excellent leadership, communication, and project management skills.

Key Metrics of Success

1. Efficient and scalable data infrastructure.

2. High data availability and reliability for business operations.

3. Effective team collaboration and low turnover rates.

4. Compliance with data governance and regulatory standards.

5. Reduced data processing times and cost optimization.