Data Engineer

Required skills

3+ years of experience in Data Engineering, Data Analytics, or a related field such as Analytics Engineering. Advanced knowledge of databases and SQL with the ability to efficiently stage, process, and transform data. Experience integrating and orchestrating data workflows with various modern data tools and systems. Experience with data modeling, ETL/ELT processes, and data warehousing solutions. Experience working with a data warehouse such as Snowflake. Experience with a data workflow orchestrator tool such as Airflow. Experience with a programming language such as Python. Experience with agentic AI. Exceptional quantitative and analytical skills. Strong communication skills and ability to collaborate with various stakeholders, both technical and non-technical.

Preferred skills

Familiarity with BI tools such as Looker, Tableau, or similar platforms. Solid dbt in production: models, tests, docs, and collaboration in a shared repo. AI-ready data: Metrics and dimensions are clear and reusable so AI-assisted analysis does not fork definitions. Experience with Salesforce data integration.

Technologies

Snowflake, Airflow, Python, agentic AI, databases, SQL, data modeling, ETL/ELT processes, data warehousing, BI tools, dbt, Salesforce data integration.

Responsibilities

Design, build, and maintain data models and pipelines that scale with the growing number of services, products, and changes in the company. Collaborate closely with Data Scientists, Data Analysts, and Business teams to understand their data needs, translating them into robust, efficient, scalable data solutions that enable ease of predictive analytics, data analysis, and metrics formulation. Maintain data documentation and definitions, building and ensuring that source-of-truth tables remain high quality for data science and reporting applications. Develop and enable integration with various data sources, allowing for more data-driven initiatives across the company. Apply best practices in data management to ensure the reliability and robustness of data utilized across various analytics applications. Set and proliferate company-wide standards for data relating to structure, quality, and expectations. Act as a liaison between the technical and non-technical teams, bridging gaps and ensuring that data solutions align with business objectives. Use agentic AI where it speeds up pipeline and quality work, without skipping validation.

Seniority

Domain

Finance, Data, Technology