Lead Data Engineer

Pay rate range - $85/hr. to $90/hr. Hybrid - 2-3 Days in Office

Job Description

Primary Accountabilities

• Leads an engineering team to meet project deadlines and priorities.
• Supervises assigned data engineering team members & activities.
• Ensures the quality, completeness, security, privacy, and integrity of data throughout the data lifecycle.
• Documents critical workflows and operational support aspects of team's responsibilities
• Develops deep understanding of data sources, granularity, availability, and limitations.
• Provides proactive technical oversight and advice to application architecture and development teams fostering re-use, design for scale, stability, and operational efficiency of data/analytical solutions.
• Creates maintainable, scalable code to load and manipulate data in the data warehouse.
• Facilitates communication upward and across project teams and business stakeholders.

Specialized Knowledge & Skills Requirements

• Demonstrated experience providing customer-driven solutions, support or service.
• Must have GCP experience for this positon.
• In-depth knowledge of SQL or NoSQL and experience using a variety of data stores (e.g. RDBMS, analytic database, scalable document stores)
• Extensive hands‑on Python programming experience, with an emphasis towards building ETL workflows and data-driven solutions.
• Able to employ design patterns and generalize code to address common use cases.
• Capable of authoring robust, high quality, reusable code and contributing to the division's inventory of libraries.
• Expertise in big data batch computing tools (e.g. Hadoop or Spark), with demonstrated experience developing distributed data processing solutions.
• Knowledge of open source machine learning toolkits, such as sklearn, SparkML, or H2O.
• Solid data understanding and business acumen in the data rich industries like insurance or financial
• Applied knowledge of data modeling principles (e.g. dimensional modeling and star schemas).
• Strong understanding of database internals, such as indexes, binary logging, and transactions.
• Experience using tools for infrastructure-as-code (e.g. Docker, CloudFormation, Terraform, etc.)
• Experience with software engineering tools and workflows (i.e. Jenkins, CI/CD, git).
• Practical experience authoring and consuming web services.