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- Transform raw data into structured datasets by designing and implementing data models that align with business requirements.
- Apply various data transformations such as cleaning, aggregating, and joining data to ensure accuracy and relevance for specific analytical tasks.
- Design and implement optimized data models that drive significant improvements in query performance, data accuracy, and storage efficiency.
- Maintain comprehensive data documentation including data features, definitions, and descriptions at each stage of the data lifecycle to facilitate understanding and consistency.
- Define and govern data modeling and design standards, tools, best practices, and related development for enterprise data models.
- Define and enforce metrics, standards, and rules for data quality management to guarantee the accuracy, completeness, and reliability of the data.
- Implement software engineering best practices like version control, data unit testing, and continuous integration to ensure robust and reliable data management processes.
- Collaborate closely with team members including data engineers, business analysts, and data scientists to gather and refine data requirements effectively, aligning them with business objectives and strategies.
- Architect scalable and efficient data structures, leveraging distributed computing technologies and microservices architecture.
- Provide strategic guidance and mentorship to other team members, fostering a culture of excellence and innovation.
What You'll Need - 7+ years of data modeling and ETL experience, specializing in architecting enterprise solutions.
- Proficiency in SQL for data querying, analysis, and manipulation.
- Expertise in conceptual, logical, and physical data modeling principles.
- Demonstrated experience with various analytical data modeling methodologies (e.g., Star, Snowflake, Kimball).
- Knowledge of data warehousing/ETL principles and relevant technologies.
- Experience with analytics tools for extracting insights from data.
- Strong communication skills for stakeholder interaction and teamwork.
- Architect scalable data structures using distributed computing and microservices.
- Provide strategic guidance and mentorship to junior team members.
- Problem-solving ability for addressing complex data challenges.
- Clear communication of technical concepts to diverse audiences.
Key Competencies and Skills - Familiarity with cloud services such as Azure, AWS, or GCP considered an asset
- Experience with cloud-based data platforms such as Snowflake, DBT, Azure Data Factory
- Experience with BI platforms like Power BI, Looker, Sisense
- Knowledge of programming languages like Python or R for data manipulation and analysis
- Experience in effectively utilizing version control systems like Git for managing code repositories, facilitating collaboration, and tracking changes across projects
- Understanding of distributed computing, APIs, and microservices architecture
- Experience with Agile and traditional SDLC delivery methodologies
- Certification in relevant analytics or engineering fields is a plus.
Education - A degree in Computer Science, Computer Engineering or its equivalent
Symend focuses on Financial Services, Telecommunications, Utilities, Banking, and Financial Technology. Their company has offices in Denver, Canada, United States, Toronto, and Calgary. They have a large team that's between employees. To date, Symend has raised $143.694M of funding; their latest round was closed on November 2022.
You can view their website at or find them on Twitter, Facebook, and LinkedIn.
Senior Analytics Engineer - Denver, United States - Symend
Description
The Opportunity
The Senior Analytics Engineer will play a crucial role in transforming data into actionable insights for our organization. You will be responsible for developing and maintaining data models, data pipelines, optimizing data architecture, and implementing data models to support our analytics initiatives.
What You'll Do