Senior Data Engineer

at  Axented

The Senior Engineer role is a key technical role responsible for influencing the design of and executing the implementation of data solutions for transaction, reporting, and analytics purposes using technologies such as SQL, Python, Postgres, Snowflake, ETL/ELT/T, data modeling tools, tuning, and AI/ML technologies. This role demands a hands-on independent approach, with the ability to understand architecture direction and to mentor less experienced engineers, especially with traditional software engineers on data topics. He/she will work closely with the lead engineers and architects to understand the business need, design, develop, and tune/optimize solutions to support the core business domain services, the integration services, the reporting & analytics platforms, AI and ML capabilities, and underlying data models supporting each, and create/update documentation of the technologies being developed. The Senior Engineer will be curious and interested in the research and use of data and data-driven technologies such as machine learning, NLP, LLMs, data warehouses and marts, data streaming, etc. In addition, the Senior Engineer will partner with his/her lead and peer Senior Engineers in back-end, front-end, cloud engineering, and other teams to ensure alignment, architectural harmony, prioritization, and share ideas. The Engineer will be responsible for the development and evolution of the WebOps data estate, from models to transactional stores to integration patterns/models to analytics platforms and use of analytics. The incumbent is a technologist who is passionate about technology and uses this inquisitive nature to anticipate and test IT industry technologies and future directions and brings the results of that passion to benefit his/her delivery.

Requirements

• 5+ years of experience in a senior data engineering role, within an agile environment.
• Expertise in SQL, NoSQL, data lakes, and integration
• Experience with programming languages (Python, R, Java, Scala, etc.) a plus.
• Expertise in data modeling and understanding of practical vs proper and performance impacts.
• Strong understanding of modern data architectures and design patterns (lambda, kappa, etc.)
• Experience in data processing using modern tools (airflow, dbt, etc.)
• Experience with relational (Postgres preferred), NoSQL, and analytic (Snowflake) data platforms.
• Proficient in data testing frameworks and practices, and dataset management techniques
• Experience implementing data governance practices ensuring data quality and integrity.
• Experience building security into the data side of the tech stack.
• Familiarity with cloud environments and their impact on cost, performance, and scalability.
• Excellent problem-solving, logical thinking, and leadership (by influence and example) skills

Responsibilities

• Design, develop, and maintain high-quality data models for transactional and analytic stores.
• Design, develop, tune, and maintain databases, DDL, and performant SQL queries.
• Review and participate in the design of integration (API, MQ, etc.) data contracts.
• Design, develop, and maintain ELT/ETL/T between data sources or environments.
• Build platform for and guide development of BI reports, and create data sets and prototypes for AI, ML, NLP, LLMs, and other advanced data-heavy capabilities using SQL, Python, etc.
• Collaborate with architects, other developers, product managers, and other stakeholders to ensure seamless integration of front-end and back-end technologies. Make recommendations for the “right” persistence engines for the right purpose.
• Maintain high standards for code quality, testing, and security practices.
• Provide technical guidance and mentorship to junior and software engineers.
• Implement and maintain data governance practices (monitoring, quality, lineage, metadata)
• Troubleshoot data-related issues and perform RCA, ensuring timely resolution to minimize impact on data availability and accuracy. Help organization plan for BCP and low RTO/RPO.
• Keep abreast of the latest trends in data, applying them to enhance performance and usability of data and the ability to capture market opportunities.
• Stay up to date with emerging trends in data technologies, applying them to improve existing solutions to provide customers with better information.
• Identifies opportunities for improvement in current technology and that of individual systems. Avoids the creation of tech debt, identifies tech debt quickly, and prioritizes the swift remediation of technical debt.
• Participates in code reviews and other activities to ensure code quality and performance.
• Help implement automated testing and deployment.

• Design, develop, and maintain high-quality data models for transactional and analytic stores.
• Design, develop, tune, and maintain databases, DDL, and performant SQL queries.
• Review and participate in the design of integration (API, MQ, etc.) data contracts.
• Design, develop, and maintain ELT/ETL/T between data sources or environments.
• Build platform for and guide development of BI reports, and create data sets and prototypes for AI, ML, NLP, LLMs, and other advanced data-heavy capabilities using SQL, Python, etc.
• Collaborate with architects, other developers, product managers, and other stakeholders to ensure seamless integration of front-end and back-end technologies. Make recommendations for the “right” persistence engines for the right purpose.
• Maintain high standards for code quality, testing, and security practices.
• Provide technical guidance and mentorship to junior and software engineers.
• Implement and maintain data governance practices (monitoring, quality, lineage, metadata)
• Troubleshoot data-related issues and perform RCA, ensuring timely resolution to minimize impact on data availability and accuracy. Help organization plan for BCP and low RTO/RPO.
• Keep abreast of the latest trends in data, applying them to enhance performance and usability of data and the ability to capture market opportunities.
• Stay up to date with emerging trends in data technologies, applying them to improve existing solutions to provide customers with better information.
• Identifies opportunities for improvement in current technology and that of individual systems. Avoids the creation of tech debt, identifies tech debt quickly, and prioritizes the swift remediation of technical debt.
• Participates in code reviews and other activities to ensure code quality and performance.
• Help implement automated testing and deployment