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Key Responsibilities and Required Skills for Lead Data Engineer

💰 $150,000 - $220,000

Data EngineeringTechnologyData & AnalyticsLeadership

🎯 Role Definition

The Lead Data Engineer is a pivotal, hybrid role that combines deep technical expertise with strategic leadership. This individual serves as the technical backbone of the data engineering team, responsible for architecting, building, and maintaining the organization's data infrastructure. More than just a senior developer, the Lead Data Engineer guides the team in best practices, mentors junior members, and translates complex business requirements into scalable, reliable, and high-performance data solutions. They are the go-to expert for data architecture decisions and play a crucial role in shaping the future of the company's data platform.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Engineer
  • Senior Software Engineer (with a data-intensive background)
  • Data Architect

Advancement To:

  • Data Engineering Manager
  • Principal Data Engineer
  • Head of Data / Director of Data Engineering

Lateral Moves:

  • Solutions Architect
  • Principal Machine Learning Engineer
  • Senior Data Platform Manager

Core Responsibilities

Primary Functions

  • Lead the architectural design, development, and operational maintenance of robust, scalable, and high-performance data pipelines and ETL/ELT processes.
  • Architect, implement, and optimize modern cloud data warehousing solutions (e.g., Snowflake, BigQuery, Redshift) and data lake architectures.
  • Mentor, guide, and provide technical leadership to a team of data engineers, fostering a culture of technical excellence, innovation, and continuous learning.
  • Establish, document, and enforce best practices for data engineering, including data modeling standards, coding conventions, data quality checks, and testing protocols.
  • Act as the primary technical liaison between the data team and stakeholders, including data scientists, analysts, and business leaders, to understand data requirements and deliver effective solutions.
  • Own the end-to-end lifecycle of critical datasets, from ingestion and processing to storage and secure serving of data for analytics and machine learning applications.
  • Proactively identify and resolve performance bottlenecks in data processing jobs, queries, and infrastructure, ensuring cost-efficiency and scalability on cloud platforms.
  • Design, build, and manage complex data orchestration workflows using tools like Apache Airflow, Dagster, or Prefect to ensure reliable and timely data delivery.
  • Implement and maintain comprehensive data quality frameworks and automated monitoring systems to ensure the accuracy, completeness, and reliability of business-critical data.
  • Continuously evaluate and recommend new data technologies, tools, and methodologies to enhance the data platform's capabilities and drive innovation.
  • Develop and maintain thorough documentation for data architecture, data flows, and operational processes to ensure knowledge sharing and maintainability.
  • Lead technical discussions, design reviews, and decision-making processes for data infrastructure and architectural choices, ensuring alignment with long-term strategy.
  • Drive the adoption of CI/CD practices and DevOps principles within the data engineering team to improve development agility and deployment reliability.
  • Engineer and manage both real-time and batch data ingestion from a wide variety of internal and external sources, including APIs, databases, and streaming platforms like Kafka or Kinesis.
  • Implement and enforce data governance and security policies in collaboration with security and compliance teams to protect sensitive information and adhere to regulations.
  • Troubleshoot and resolve complex data pipeline and platform issues, performing root cause analysis and implementing preventative measures to minimize future incidents.
  • Act as a subject matter expert on the organization's data landscape, providing expert guidance and support to other teams across the business.
  • Lead the technical execution of migrating legacy data systems to modern, cloud-native data platforms with minimal disruption to business operations.
  • Develop and institutionalize logical and physical data models that are optimized for analytical performance and diverse business reporting needs.
  • Partner with product managers and other engineering leads to define the technical roadmap and project priorities for the data platform.
  • Automate infrastructure provisioning and configuration management using Infrastructure as Code (IaC) tools like Terraform or CloudFormation.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL and Data Modeling: Expert-level proficiency in writing complex, optimized SQL queries and designing efficient, scalable data models (e.g., star schema, snowflake schema, Data Vault).
  • Programming Languages: Deep expertise in Python for data manipulation (e.g., Pandas, Polars) and building data applications. Professional experience with Scala or Java is a strong asset.
  • Big Data Technologies: Extensive hands-on experience with distributed computing frameworks like Apache Spark and platforms such as Databricks or Amazon EMR.
  • Cloud Data Platforms: Proven experience with at least one major cloud provider (AWS, GCP, Azure) and their core data services (e.g., AWS S3, Glue, Redshift; GCP Cloud Storage, Dataflow, BigQuery).
  • Modern Data Warehousing: Demonstrated ability to architect, implement, and manage modern cloud data warehouses like Snowflake, Google BigQuery, or Amazon Redshift.
  • Workflow Orchestration: High proficiency with designing and managing complex dependencies in data pipeline orchestration tools such as Apache Airflow, Dagster, or Prefect.
  • Streaming Data Technologies: Solid experience with real-time data ingestion and processing using tools like Apache Kafka, Amazon Kinesis, or Spark Streaming.
  • Infrastructure as Code (IaC): Practical knowledge of using tools like Terraform or AWS CloudFormation to automate the provisioning and management of data infrastructure.
  • CI/CD & DevOps for Data: A firm grasp of applying continuous integration/continuous deployment pipelines (e.g., using GitHub Actions, Jenkins, GitLab CI) to data engineering projects.
  • Containerization: Working familiarity with containerization technologies like Docker and container orchestration systems such as Kubernetes.
  • Data Governance and Quality: Experience implementing frameworks and tools for data lineage, cataloging, and ensuring data quality across the platform.

Soft Skills

  • Leadership & Mentorship: A natural ability to guide, inspire, and develop the technical skills of team members.
  • Strategic Thinking: The capacity to see the bigger picture, anticipate future data needs, and make architectural decisions that support long-term business goals.
  • Communication & Stakeholder Management: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical audiences.
  • Problem-Solving: A systematic and analytical approach to diagnosing and resolving complex technical issues under pressure.
  • Collaboration: A team-player mindset with a proven track record of working effectively with cross-functional teams.
  • Project Ownership: A strong sense of responsibility and accountability for the success of projects from conception to completion.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's Degree in a relevant technical field.

Preferred Education:

  • Master's Degree in a relevant technical field.

Relevant Fields of Study:

  • Computer Science
  • Software Engineering
  • Information Systems
  • Statistics or a related quantitative field

Experience Requirements

Typical Experience Range:

  • 7-10+ years of professional experience in data engineering or a related software engineering field.

Preferred:

  • At least 2-3 years of proven experience in a senior or lead data engineering role, with a track record of leading the design and delivery of large-scale data projects on a modern data stack.
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