Vacancy caducado!
- Develop and lead a team of Data Engineers in collaboration with Data Science, Data Warehousing, and Business Intelligence to create, test, scale and monitor enterprise class data pipelines. Cultivate a generative culture that values continuous improvement through experimentation and self-guided learning. Ensure agile teams are properly staffed to deliver on time by defining clear data engineering strategies and principles, maintaining a roadmap for execution, and actively secure its implementation through the scrum process.
- Act as a positive coach/mentor for broader organization to develop stronger understanding of the data architecture and software design patterns that create scalable, maintainable, well-designed analytics solutions. Acts as a facilitator of complex technical topics that require cross-functional consultation.
- Relentlessly identify and remove friction in the data engineering development lifecycle to improve developer experience and increase velocity. Influence, develop, and sustain large-scale analytics architecture required for delivering Data Engineering work product. Establish software architecture and software design patterns to write scalable, maintainable, well-designed code.
- Research and evaluate tools in data transformation, automated CI/CD, data quality monitoring, and data serving.
- Work in cross-functional, geographically distributed agile teams of highly skilled data engineers, software/machine learning engineers, data scientists, DevOps engineers, designers, product managers, technical delivery teams, and others to continuously innovate analytic solutions.
- Independently execute and review others exploratory data analysis to ensure data quality and relational characteristics
- Design, develop, and review real-time/bulk data pipelines from a variety of sources (streaming data, APIs, data warehouse, messages, images, video, etc)
- Develop, review, and secure documentation of Data Lineage and Data Dictionaries to create a broad awareness of the enterprise data model and its applications
- Actively champion and apply best practices within DataOps (Version Control, P.R. Based Development, Schema Change Control, CI/CD, Deployment Automation, Test Automation, Shift left on Security, Loosely Coupled Architectures, Monitoring, Proactive Notifications)
- Coordinate with other teams to design optimal patterns for data ingest, transformation, and egress as well as lead and coordinate data quality initiatives and troubleshooting
- Guide problem solving sessions, ensuring alignment to North Star. Provide mentorship and coaching to junior team members.
- Proactively adapt team makeup to increase project velocity and solution quality.
- Bachelor’s degree in engineering, computer science, analytical field (Statistics, Mathematics, etc.) or closely related discipline and eight (8) years of relevant work experience including five (5) years of project management/leadership experience; OR
- Master’s in engineering, computer science, analytical field (Statistics, Mathematics, etc.) or closely related discipline and six (6) years of relevant work experience including three (3) years of project management/leadership experience; OR
- Ph.D. in engineering, computer science, analytical field (Statistics, Mathematics, etc.) or closely related discipline and four (4) years of relevant work experience including three (3) years of project management/leadership experience
- Expert practitioner of SQL development with experience designing high quality, production SQL codebases
- Expert practitioner of Python development with experience designing high quality, production Python codebases
- Expert in data engineering, software engineering, and ML systems architecture
- Experience applying software development best practices in data engineering projects, including Version Control, P.R. Based Development, Schema Change Control, CI/CD, Deployment Automation, Test Driven Development/Test Automation, Shift left on Security, Loosely Coupled Architectures, Monitoring, Proactive Notifications using Python and SQL
- Data science experience wrangling data, model selection, model training, modeling validation, e.g., Operational Readiness Evaluator and Model Development and Assessment Framework, and deployment at scale
- Experience with Agile and DevOps software development principles/methodologies, keep team focus on deliver business value
- Experience leading and developing teams
- Experience with at least one modern Machine Learning framework such as TensorFlow, Pytorch, Keras, etc.
- Understanding of Machine Learning fundamentals
- Experience influencing and building mindshare convincingly with any audience. Confident and experienced in public speaking to large audiences
- Working knowledge of Azure Stream Architectures, DBT, Schema Change tools, Data Dictionary tools, Azure Machine Learning Environment, GIS Data
- Working knowledge of Software Engineering and Object Orient Programming Principles
- Working knowledge of Distributed Parallel Processing Environments such as Spark or Snowflake
- Working knowledge of Edge Analytics, embedded systems, or computer vision.
- Working knowledge of problem solving/root cause analysis on Production workloads
- Experience working in Data Science Product Teams, managing Design, Implementation of data projects that utilize big data, advanced analytics and machine learning technologies.
- Working knowledge of Agile, Scrum, and Kanban
- Working knowledge of workflow orchestration using tools such as Airflow, Prefect, Dagster, or similar tooling
- Experience building OR implementing a feature store
- Working knowledge with CI/CD and automation tools like Jenkins or Azure DevOps
- Experience with containerization tools such as Docker
Vacancy caducado!