Analytics Engineer
Upload My Resume
Drop here or click to browse · PDF, DOCX, DOC, RTF, TXT
Requirements
• Bachelor’s degree in Computer Science, Engineering, or a related field. • Intermediate/Advanced English proficiency. • Advanced experience in data modeling with dbt (Data Build Tool). • Proven experience as an Analytics Engineer or Data Engineer. • Experience in the analytics workflow (understanding business requirements, data transformation, quality checks, building pipelines, analyses, and dashboards). • Advanced SQL knowledge with proven track record, and experience with relational and non-relational databases. • Hands-on experience with data integration tools like Stitch. • Experience with AI tools for more efficient deliverables. • Ability to create intuitive reports and dashboards using Metabase (Looker or Tableau experience will help). • Strong analytical skills with the ability to collect, organize, analyze, and disseminate meaningful information. • Excellent communication and collaboration skills to work effectively with multidisciplinary teams. • Knowledge of programming languages such as Python. • Experience with data orchestrators like Airflow. • Knowledge of AWS data stack. • Infrastructure as Code (IaC) with Terraform. • Relevant certifications in data engineering or data analysis.
Responsibilities
• Translate business needs in data solutions & products aligned with the data platform strategy • Negotiate with stakeholders a impact driven data strategy • Mentore Swilers on data usage and techniques for better data knowledge and data decentralization. • Develop ETL/ELT routines and data pipelines. • Implement CI/CD routines for data quality. • Create, manage, and document data models using dbt (Data Build Tool) to ensure data quality and efficiency. • Use Metabase and to create visual dashboards and reports that provide actionable insights for business teams. • Design and implement efficient data storage solutions to support analytics, including the use of Snowflake. • Collaborate with cross-functional teams to understand business needs and translate them into clear technical requirements. • Monitor the integrity and performance of data systems and make necessary adjustments. • Ensure compliance with data security and privacy policies.