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Mid-level generalist data-engineering role with broad stack in metro markets increases candidate competition.
Core database and data engineering skills are broadly transferable across industries.
Explicit 5-8 years plus mandatory database and data-engineering stack increases screening strictness.
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Design, develop, optimize, and maintain scalable relational and NoSQL database systems and cloud-based data warehouses using technologies like PostgreSQL, Oracle, MongoDB, Snowflake, BigQuery, and Redshift.
Build and enhance ETL/ELT data pipelines and automation scripts primarily using Python and/or Node.js; implement schema design, indexing, query tuning, and data security practices.
Collaborate with analytics, BI, product, and engineering teams to support data-driven solutions, perform exploratory data analysis, and provide guidance on best data engineering and architecture practices.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, IT, or related field.
5-8 years of experience in database engineering, data warehousing, or data engineering roles.
Strong expertise in SQL & PL/SQL, PostgreSQL, Oracle, MySQL/MariaDB, MongoDB, modern cloud data warehouses (Snowflake, BigQuery, Redshift), and programming skills in Python and/or Node.js for data pipelines.
Experience with data modeling, ETL/ELT concepts, and DevOps practices for databases (CI/CD, Git, Data Ops, versioning).
Experienced database and data engineering professional comfortable designing and optimizing large-scale transactional and cloud data platforms in dynamic, cross-functional environments.
Proficient in both backend programming (Python/Node.js) and database architectures, with practical knowledge of data warehousing and automation best practices.
Capable of collaborating with analytics and engineering teams to enable business intelligence and machine learning use cases through robust data solutions and pipelines.