A Lead Data Engineer is essential for designing, building, and managing robust data infrastructures that enable organizations to make data-driven decisions. As companies rely more on big data to drive business strategy and insights, the role of a Lead Data Engineer is vital for setting up scalable data pipelines, maintaining data quality, and ensuring efficient data processing.

What is a Lead Data Engineer?

A Lead Data Engineer is responsible for designing, developing, and optimizing large-scale data pipelines and storage solutions. They manage teams of data engineers, collaborate with data scientists, and work closely with business stakeholders to understand data requirements. Lead Data Engineers ensure the integrity and accessibility of data, implementing best practices for data governance, scalability, and security. With expertise in ETL processes, cloud environments, and data infrastructure, they create data solutions that support analytics, machine learning, and business intelligence needs across the organization.

Lead Data Engineer Responsibilities Include

  • Designing and implementing scalable data pipelines to collect, process, and store large volumes of structured and unstructured data.
  • Leading a team of data engineers, mentoring them in best practices, and overseeing their work on data projects.
  • Collaborating with data scientists, analysts, and business stakeholders to understand data needs and align on requirements.
  • Building and maintaining data infrastructure in cloud platforms like AWS, Google Cloud, or Azure.
  • Developing ETL processes to transform raw data into accessible and usable formats.
  • Ensuring data quality, integrity, and consistency through validation checks and data governance practices.
  • Managing data security and access control to protect sensitive information.
  • Monitoring pipeline performance, optimizing for efficiency, and troubleshooting issues.
  • Keeping up-to-date with industry trends and emerging technologies to continually enhance data processes.
  • Creating documentation for data processes, infrastructure, and best practices.

Job Title: Lead Data Engineer

Job Introduction

We are looking for an experienced Lead Data Engineer to join our team and help us build and manage scalable data infrastructure. The ideal candidate will have expertise in ETL processes, cloud platforms, and data pipeline design, along with a passion for leading and mentoring a team. If you are committed to enabling data-driven decision-making and creating robust data solutions, we’d love to hear from you.

Responsibilities:

  • Design, implement, and maintain scalable data pipelines to process large volumes of data.
  • Lead and mentor a team of data engineers, ensuring high-quality deliverables and best practices.
  • Build and maintain data infrastructure in cloud platforms (e.g., AWS, Google Cloud, Azure).
  • Develop efficient ETL processes to transform raw data into usable formats for analysis and reporting.
  • Ensure data quality, integrity, and compliance with organizational and regulatory standards.
  • Work closely with data scientists, analysts, and business stakeholders to understand data requirements.
  • Implement data security measures and access controls to safeguard sensitive information.
  • Monitor data pipeline performance, troubleshoot issues, and optimize for efficiency.
  • Create and maintain detailed documentation of data architecture, processes, and best practices.
  • Stay current with industry trends and emerging technologies to continually improve data engineering practices.

Requirements:

  • Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field; Master’s degree preferred.
  • 5+ years of experience in data engineering, with at least 2 years in a leadership role.
  • Strong proficiency in ETL tools and frameworks, such as Apache Airflow, Talend, or Informatica.
  • Experience with programming languages like Python, SQL, and Java.
  • Expertise in cloud data platforms (AWS, Google Cloud, Azure) and their data services.
  • Knowledge of data warehousing solutions, such as Snowflake, Redshift, or BigQuery.
  • Familiarity with big data technologies like Spark, Hadoop, or Kafka.
  • Strong understanding of data governance, data quality, and compliance standards.
  • Excellent communication and team leadership skills.
  • Analytical and problem-solving abilities with a focus on data infrastructure.

Conclusion

This Lead Data Engineer job description template outlines the skills, responsibilities, and qualifications needed for the role, helping you attract the right candidates. With Cleveri’s AI-driven Candidate Screening and Video Interviewing platform, you can efficiently assess both technical expertise and cultural fit, streamlining the hiring process. Cleveri’s platform enables you to identify top talent who can design and manage data infrastructure to support your organization’s data-driven objectives.