Archer - Lead Data Engineer

Archer - Lead Data Engineer
Archer is a leading provider of integrated risk management (IRM) solutions that enable customers to improve strategic decision-making and operational resilience with a modern technology platform that supports qualitative and quantitative analysis driven by both business and IT impacts. As true pioneers in governance, risk and compliance (GRC) software, Archer remains solely dedicated to helping customers manage risk and compliance domains, from traditional operational risk to emerging issues such as ESG. With over 20 years in the risk management industry, the Archer customer base represents one of the largest pure risk management communities globally, with more than 1,200 customers including more than 50% of the Fortune 500. Learn more at www.ArcherIRM.com.
Principal Accountabilities:

• Take the lead on creating, defining, and driving the Data Engineering solutions to meetfunctional business requirements.
• Data engineering needs can include Data aggregation/creation, data cleaning/manipulation, data science (e.g., geospatial, machine learning, predictive modeling, etc.), and visualizations.
• Solve a wide variety of complex analytical challenges sometimes dynamically balancing multiple internal projects at one time.
• Supporting project teams with their analytics work in a "consultancy/expert" capacity on best practices with respect to data & analytics engineering (e.g., on-prem databases & systems, data storage & warehousing, data management, big data principles, analytics app prototyping, ETL)
• Develop and drive the Data Engineering roadmap needed to support the business.
• Collaborate with a variety of stakeholders to continuously innovate on the tools, services, and data assets we can offer.
• Serve as the lead technical expert and thought leader in helping to innovate and develop offerings that require / benefit from advanced analytics skills or capabilities.
• Provide technical expertise and thought leadership on developing analytical tools, services, and data assets, and contribute to building these areas directly when applicable.
• Create production-quality data pipelines to develop and deploy scalable data science projects.
• Stay up to date on best-in-class software, tools, and techniques to ensure that we can provide best-in-class solutions.
• Support development and upskilling of staff on relevant software, tools, and techniques
• Develop and drive the technical roadmap on Data Engineering capabilities and infrastructure to ensure we are operating a best-in-class Data & Analytics function.

Principal Responsibilities:

• Degree in a quantitative and/or business discipline or previous business experience preferred, examples include: Computer Science, Engineering, Information Technology, Data Science, Statistics, Mathematics, Operations Research, Economics
• Minimum 5 years of experience in applied Data Engineering; deep expertise in Data Engineering, as well as strong business and strategic analytical skills
• Ability to understand and articulate requirements to/from technical and non-technical audiences working alongside a data science & engineering team.
• Strong SQL & Python knowledge and familiarity with Big Data tools such as Spark & Scala; familiarity with Alteryx and Tableau
• Experience with data modeling, data structures (e.g., relational, and non-relational data), databases, and ETL/ELT processes and an in-depth understanding of large-scale data sets including both structured and unstructured data.
• Experience with designing, implementing, and delivering scalable data solutions and pipelines on one of the cloud platforms (e.g., AWS, Azure, GCP) and on-prem platforms such as Microsoft SQL Server
• Experience with building and deploying proprietary cloud-based analytics web apps.
• Hands-on experience in the development, deployment, and operation of integration technologies (e.g., APIs)
• Proficiency with data warehouses (e.g., Snowflake, Teradata, Redshift, Hadoop, BigQuery, etc.)
• Experience with containerization technologies (e.g., Docker, Kubernetes)
• Experience with DevOps, Git, CI/CD
• Experience directly managing another individual or a team.
• Prior experience in Strategic Data Consulting is preferred.

Education and Experience:
typically requires 12+ years of related experience in a professional role with a Bachelor's degree; or 8+ years with a Master's degree; or 5+ years with a PhD; or equivalent experience
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