Data Engineering Lead
7 days ago
Senior Data Engineer - Job Summary
About the RoleRiverPoint is seeking an experienced Data Engineering Lead to spearhead the design, development, and implementation of our Enterprise Analytics platform. This key position will be responsible for leading large-scale ETL pipelines, data processing frameworks, and distributed data warehouses running in the cloud.
Key Responsibilities- Strategic Capabilities and Architecture Design: Collaborate with Technical Leadership to design and develop architectural strategies for Data Warehousing, Big Data, and Data Analytics.
- ETL Pipelines and Data Processing: Architect and develop code for large-scale ETL pipelines using data processing frameworks supporting distributed data warehouse and analytic systems running in the cloud.
- Logical and Physical Data Models: Lead side-by-side design sessions with business experts and engineers to understand product and business requirements, designing proper conceptual, logical, and physical data models.
- Data Pipeline Engineering: Spearhead efforts with multiple data providers and downstream consumers to engineer efficient, traceable, and fault-tolerant batch and streaming data pipelines supporting complex analytical analysis and enterprise-wide Business Intelligence applications.
- Collaboration with Data Science Team: Lead efforts to develop and implement production-grade code/pipelines leveraging advanced statistical routines, Machine Learning, and AI technologies.
- Data Quality Automation: Oversee improvements on 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 and Security: 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.
- Escalated Issues and Performance Resolution: Resolve escalated issues related to complex data anomalies and performance, implementing adjustments, conducting after-action reviews to determine root cause/corrective measures, and ensuring 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.
- Documentation and Code Reviews: Ensure documentation is complete and up-to-date with regard to standards, best practices, and technical specifications, leading design and code review sessions and providing feedback and mentorship to peers.
- Industry Education and Thought Leadership: Educate the BI & Analytics team on the latest industry technologies, trends, and strategies, providing thought leadership around approaches, patterns, and best practices in the Data Engineering domain.
- A minimum of 10 years of progressive database development and integration experience.
- Demonstrated experience in designing and implementing large-scale logical and physical data models with expert knowledge of Kimball methodologies.
- Demonstrated advanced/expert level experience in multiple relational (RDMS) and non-relational (NoSQL) data platforms.
- Demonstrated advanced scripting knowledge with Jupyter/Python or R.
- Demonstrated expert experience with data integrations and data processing for Business Intelligence and analytics workloads.
- Experience with software versioning tools, including Git, SVN, or related.
- Advanced experience with AWS S3 or other distributed object stores, MPP Data Warehousing, Elastic MapReduce.
- Experience implementing statistical models, ML, and other AI technologies into production data.
- Experience developing containerized solutions.
- Demonstrated experience implementing Big Data pipelines, including Spark, Hive, Sqoop, or related technologies.
- Experience implementing streaming data pipelines to support real-time and near-real-time decision making.
- Understanding of Software Development Life Cycle (SDLC) methodologies, such as Agile and Waterfall.
- Proven track record of designing data platforms leveraging large complex data sets.
This position requires a Bachelor's Degree in Computer Science, Computer Information Systems, or related field, or equivalent experience. Data or cloud-related certifications are a plus.
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