Data Engineer, Analytics

With a company culture rooted in collaboration, expertise and innovation, we aim to promote progress and inspire our clients, employees, investors and communities to achieve their greatest potential. Our work is the catalyst that helps others achieve their goals. In short, We Enable Possibility℠. Join the fast growing and forward-thinking Strategic Analytics team at Arch. Our team develops innovative predictive models and analytical tools to improve profitability and growth. Data is at the core of everything we do, and we leverage advanced technologies such as Snowflake, Spark, Python, R, and Azure Data Factory to build best-in-class datasets that fuel insights and decisions. As a Data Engineer on our Strategic Analytics team, you will play a crucial role in developing complex analytic and model features, data structures, and pipelines. Your work will empower data scientists, business leaders, and stakeholders to make smarter decisions and set strategic priorities. As a key member of the Strategic Analytics team, you will advance our data and feature engineering capabilities helping the team analyze data better and faster. What's in it for You Be at the cutting edge of data engineering with access to the latest tools and technologies. Collaborate on high-profile analytics projects that shape the company's strategy. Join a passionate, entrepreneurial minded team committed to innovation and continuous improvement. Enjoy the flexibility of remote work, with preference given to those who can work EST hours. Responsibilities • Collaborate with data scientists to understand and solve business problems by creating optimal features and data structures for analysis. • Build strong partnerships with peers across the organization to support data-related goals. • Continuously apply insights from data engineering to develop best practices, increasing the efficiency and impact of future projects. • Ensure data quality and integrity through appropriate testing, validation, reconciliation, and documentation. • Recommend, communicate, and implement appropriate solutions to address data quality issues. • Discover and explore new technologies, tools, and data sources with curiosity and creativity. • Identify appropriate technologies to automate data ingestion and perform data transformation to link external sources to internal data. • Manage both explicit and indistinct requirements throughout the data asset development lifecycle.