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Chief Data Architecture Engineer
2 weeks ago
We are seeking a highly skilled Chief Data Architecture Engineer to join our team at RiverPoint. This is an exceptional opportunity to lead design, development, and implementation efforts on our Enterprise Analytics platform.
Key Responsibilities- Design and Implementation: Collaborate with Technical Leadership to define strategic capabilities and architecture for Data Warehousing, Big Data, and Data Analytics.
- Data Engineering: Develop and implement large-scale ETL pipelines using data processing frameworks, distributed data warehouses, and distributed analytic systems running in the cloud.
- Technical Leadership: Lead side-by-side design sessions with business experts and engineers to understand product and business requirements, design conceptual, logical, and physical data models, and socialize and implement them.
- Pipeline Engineering: Spearhead efforts to engineer efficient, traceable, and fault-tolerant batch and streaming data pipelines supporting complex analytical analysis and enterprise-wide Business Intelligence applications.
- Collaboration and Development: Work closely with the Data Science team to develop and implement production-grade code/pipelines in languages including Python, leveraging advanced statistical routines, Machine Learning, and other AI technologies.
- Data Quality Automation: Oversee and improve approaches to monitor and support data quality automation efforts, working alongside QA Engineering to implement quality check routines as part of data processing frameworks and validate information flow.
- Infrastructure Engineering: Drive engineering efforts for Data Warehouse and Big Data infrastructure needs, including automation of system builds, security requirements, performance requirements, and logging/monitoring in collaboration with DevOps engineers.
- Issue Resolution: Resolve escalated issues related to complex data anomalies and performance, implement adjustments, conduct after-action reviews to determine root cause/corrective measures, and ensure knowledge transfer to operations support teams.
- Mentorship and Guidance: Provide mentorship and guidance to help grow the technical depth of junior Data Engineers and across the BI & Analytics team.
- Experience: A minimum of 10 years of progressive database development and integration experience.
- Data Modeling: Demonstrated experience in designing and implementing large-scale logical and physical data models with expert knowledge of Kimball methodologies.
- Database Platforms: Demonstrated advanced/expert-level experience in multiple relational (RDBMS) and non-relational (NoSQL) data platforms.
- SQL Tuning: Demonstrated advanced experience in SQL tuning, indexing, distribution, partitioning, data access patterns, and scaling strategies.
- Scripting Knowledge: Demonstrated advanced scripting knowledge with Jupyter/Python or R.
- Data Integrations: Demonstrated expert experience with data integrations and data processing for business intelligence and analytics workloads.
- Software Versioning: Experience in software versioning tools including Git, SVN, or related.
- Distributed Object Stores: Advanced experience with AWS S3 or other distributed object stores, MPP Data Warehousing, Elastic MapReduce.
- Statistical Models: Experience implementing statistical models, ML, and other AI technologies into production data.
- Containerization: Experience developing containerized applications.