Head of Data Engineering Job Description Template

As Head of Data Engineering, you will be tasked to grow a team of data engineers and partner with stakeholders to build out the data foundations. This is an opportunity to use your engineering skills to lead a team of data engineers to design and deliver solutions for our internal and external facing business lines. You will get a chance to leverage your strategic planning and technical knowledge of data engineering, tools and data architecture definition to improve the effectiveness of data pipelines. You’ll be responsible for strategic and operational leadership within data and across the engineering leadership team. Success in this role looks like leading the team to provide unique data pipeline solutions for structured, semi-structured, and unstructured data sources by bringing tactics that enable flow of data across the company in a highly complex and regulated environment.

Typical Duties and Responsibilities

• Build and manage a team of talented data engineers with the ability to not only keep up with, but also pioneer, in this space
• Collaborate with and influence leadership to directly impact company strategy and direction
• Develop new techniques and data pipelines that will enable various insights for internal and external customers
• Develop deep partnerships with engineering and product teams to deliver on major cross-functional measurements and testing
• Communicate effectively to all levels of the organization, including executives
• Provide success in partnering teams with dramatically varying backgrounds, from the highly technical to the highly creative
• Design a data engineering roadmap and execute the vision behind it
• Hire, lead, and mentor a world-class data team
• Partner with other business areas to co-author and co-drive strategies on our shared roadmap
• Oversee the movement of large amounts of data into our data lake
• Establish a customer-centric approach and synthesize customer needs
• Own end-to-end pipelines and destinations for the transfer and storage of all data
• Manage 3rd-party resources and critical data integration vendors

Education

• Bachelor’s degree in computer science, computer engineering, or a related field

Required Skills and Experience

• Experience creating large-scale data engineering pipelines, data-based decision-making and quantitative analysis tools and software
• Experience with Visualization Tools like Tableau, Power BI, D3 and hands-on to code version control systems (git)
• Experience with CICD, data architectures, pipelines, quality, and code management
• Experience in data science, including predictive modeling
• Experience with complex, high volume, multi-dimensional data, as well as machine learning models based on unstructured, structured, and streaming datasets
• Experience with SQL and NoSQL databases
• Experience creating, testing, and supporting production software and systems 
• Proven track record of identifying and resolving performance bottlenecks for production systems
• Experience designing and developing data lake, data warehouse, ETL and task orchestrating systems
• Strong leadership, communication, time management and interpersonal skills
• Proven architectural skills in data engineering
• Experience leading teams developing production-grade data pipelines (using Spark, Flink, dbt, etc.) on web-scale datasets 
• Experience designing a large data lake and lake house experience, managing data flows that integrate information from various sources into a common pool implementing data pipelines based on the ETL model
• Experience with common data languages (e.g. Python, Scala) and data warehouses (e.g. Redshift, BigQuery, Snowflake, Databricks)

Preferred Qualifications

• Experience managing real-time data pipelines with low latency SLAs
• Databricks expertise
• Experience working with large datasets, cloud (e.g. AWS/GCP), Infrastructure as Code and developing machine learning models in a cloud environment
• Experience using deep learning and deep reinforcement learning or natural language processing in a production environment