Lead Data Engineer(Snowflake/DBT)

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Requirements:

• 4+ years of extensive development experience using snowflake or similar data warehouse technology
• Working experience with dbt and other technologies of the modern data stack, such as Snowflake, Apache Airflow, Fivetran, AWS, git ,Looker
• Experience in agile processes, such as SCRUM
• Extensive experience in writing advanced SQL statements and performance tuning them
• Experience in Data Ingestion techniques using custom or SAAS tool like fivetran
• Experience in data modelling and can optimise existing/new data models
• Experience in data mining, data warehouse solutions, and ETL, and using databases in a business environment with large-scale, complex datasets
• Experience architecting analytical databases (in Data Mesh architecture) is added advantage
• Experience working in agile cross-functional delivery team
• High development standards, especially for code quality, code reviews, unit testing, continuous integration and deployment
• Strong technical documentation skills and the ability to be clear and precise with business users
• Business-level of English and good communication skills
• Basic understanding of various systems across the AWS platform ( Good to have )
• Preferably, you have worked in a digitally native company, ideally fintech

Responsibilities:

• Developing end to end ETL/ELT Pipeline working with Data Analysts of business Function.
• Designing, developing, and implementing scalable, automated processes for data extraction, processing, and analysis in a Data Mesh architecture
• Mentoring Fother Junior Engineers in the Team
• Be a “go-to” expert for data technologies and solutions
• Ability to provide on the ground troubleshooting and diagnosis to architecture and design challenges
• Troubleshooting and resolving technical issues as they arise
• Looking for ways of improving both what and how data pipelines are delivered by the department
• Translating business requirements into technical requirements, such as entities that need to be modelled, DBT models that need to be build, timings, tests and reports
• Owning the delivery of data models and reports end to end
• Perform exploratory data analysis in order to identify data quality issues early in the process and implement tests to ensure prevent them in the future
• Working with Data Analysts to ensure that all data feeds are optimised and available at the required times. This can include Change Capture, Change Data Control and other “delta loading” approaches
• Discovering, transforming, testing, deploying and documenting data sources
• Applying, help defining, and championing data warehouse governance: data quality, testing, coding best practices, and peer review
• Building Looker Dashboard for use cases if required