Lead Data Engineer - Client Deployment

October 2

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Logo of Demyst

Demyst

Credit Scoring β€’ Analytics β€’ Alternative Data β€’ Big Data β€’ Fraud

51 - 200 employees

🏒 Enterprise

☁️ SaaS

πŸ’° Funding Round on 2021-07

Description

β€’ OUR SOLUTION β€’ At Demyst, we're transforming the way enterprises manage data, eliminating key challenges and driving significant improvements in business outcomes through data workflow automation. Due to growing demand, we're expanding our team and seeking talented individuals to help us scale. β€’ Our platform simplifies workflows, eliminating the need for complicated platforms and expensive consultants. With top-tier security and global reach, we're helping businesses in banking and insurance achieve digital transformation. If you're passionate about data and affecting change, Demyst is the place for you. β€’ THE CHALLENGE β€’ Demyst is seeking a Lead Engineer with a strong data engineering focus to play a pivotal role in delivering our next-generation data platform to leading enterprises across North America. In this role, you will lead a team of data engineers with a primary focus on data integration and solution deployment. You will oversee the development and management of data pipelines, ensuring they are robust, scalable, and reliable. This is an ideal opportunity for a hands-on data engineering leader to apply technical, leadership, and problem-solving skills to deliver high-quality solutions for our clients. β€’ Your role will involve not only technical leadership and mentoring but also actively contributing to coding, architectural decisions, and data engineering strategy. You will guide your team through complex client deployments, from planning to execution, ensuring that data solutions are effectively integrated and aligned with client goals. β€’ Demyst is a remote-first company. The candidate must be based in the United States.

Requirements

β€’ Bachelor's degree or higher in Computer Science, Data Engineering, or related fields. Equivalent work experience is also highly valued. β€’ 5-10 years of experience in data engineering, software engineering, or client deployment roles, with at least 3 years in a leadership capacity. β€’ Strong leadership skills, including the ability to mentor and motivate a team, lead through change, and drive outcomes. β€’ Expertise in designing, building, and optimizing ETL/ELT data pipelines using Python, JavaScript, Golang, Scala, or similar languages. β€’ Experience in managing large-scale data processing environments, including Databricks and Spark. β€’ Proven experience with Apache Airflow to orchestrate data pipelines and manage workflow automation. β€’ Deep knowledge of cloud services, particularly AWS (EC2/ECS, Lambda, S3), and their role in data engineering. β€’ Hands-on experience with both SQL and NoSQL databases, with a deep understanding of data modeling and architecture. β€’ Strong ability to collaborate with clients and cross-functional teams, delivering technical solutions that meet business needs. β€’ Proven experience in unit testing, integration testing, and engineering best practices to ensure high-quality code. β€’ Familiarity with agile project management tools (JIRA, Confluence, etc.) and methodologies. β€’ Experience with data visualization and analytics tools such as Jupyter Lab, Metabase, Tableau. β€’ Strong communicator and problem solver, comfortable working in distributed teams.

Benefits

β€’ Operate at the forefront of the data management innoivation, and work with the largest industry players in an emerging field that is fueling growth and technological advancement globally β€’ Have an outsized impact in a rapidly growing team, offering real autonomy and responsibility for client outcomes β€’ Stretch yourself to help define and support something entirely new β€’ Distributed team and culture, with fully flexible working hours and location β€’ Collaborative, inclusive, and dynamic culture β€’ Generous benefits and compensation plans β€’ ESOP awards available for tenured staff β€’ Join an established, and scaling data technology business

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