Lead, Data Engineer (Client Deployment) (United States)

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Requirements:

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

Responsibilities:

• Lead the configuration, deployment, and maintenance of data solutions on the Demyst platform to support client use cases.
• Supervise and mentor the local and distributed data engineering team, ensuring best practices in data architecture, pipeline development, and deployment.
• Recruit, train, and evaluate technical talent, fostering a high-performing, collaborative team culture.
• Contribute hands-on to coding, code reviews, and technical decision-making, ensuring scalability and performance.
• Design, build, and optimize data pipelines, leveraging tools like Apache Airflow to automate workflows and manage large datasets effectively.
• Work closely with clients to advise on data engineering best practices, including data cleansing, transformation, and storage strategies.
• Implement solutions for data ingestion from various sources, ensuring the consistency, accuracy, and availability of data.
• Lead critical client projects, managing engineering resources, project timelines, and client engagement.
• Provide technical guidance and support for complex enterprise data integrations with third-party systems (e.g., AI platforms, data providers, decision engines).
• Ensure compliance with data governance and security protocols when handling sensitive client data.
• Develop and maintain documentation for solutions and business processes related to data engineering workflows.
• Other duties as required.