Workato16 days ago

Analytics Engineer

Salary not specified

MARKET

13,534 ₽median for this role

Tech Lead / Team Lead · 166 jobs with disclosed pay

5,250half of the offers: 9,771–18,1061.2 млн

The employer didn't disclose pay — compare with the market yourself.

Singapore

Responsibilities

01Design, develop, and own scalable and maintainable data models using dbt (Data Build Tool)
02Enforce and evolve internal dbt conventions and best practices, optimizing the codebase for cleanliness, performance, and cost-efficiency
03Own and enforce data quality and consistency by implementing robust testing, validation, and cleaning processes on mission-critical source tables
04Implement and manage data monitoring and alerting solutions to ensure data flows and transformations are performing optimally and accurately
05Create and maintain comprehensive data documentation and definitions (data dictionaries, process flows) to ensure data literacy, trust, and discoverability
06Partner with data engineers, product analysts, GTM data teams, and other stakeholders to strategically align data insights with product improvements and business objectives
07Communicate complex data architecture, patterns, and analytical conclusions effectively to both technical and non-technical audiences
08Act as a data champion, evangelizing and guiding business users on the most efficient and reliable ways to leverage our data products
09Lead the research and evaluation of new tools and technologies, like GenAI, for enhancing data engineering, orchestration, and analysis workflows
10Develop and test high-impact prototypes that demonstrate the potential of emerging technologies (e.g., GenAI) to augment and improve our product usage datasets and data platform capabilities

012+ years of experience in an Analytics Engineering or Data Warehousing role
02Expert proficiency in SQL, including advanced techniques like window functions and proven ability in query performance optimization
03Demonstrated expertise in dbt (Data Build Tool) for designing, developing, and maintaining complex data models
04Strong functional knowledge of a modern cloud data warehouse (e.g., Snowflake, BigQuery)
05Proven ability to apply data engineering best practices, including version control (Git/GitHub), modular coding, and automated testing
06Strong understanding of data modeling principles (e.g., star/snowflake schemas, Slowly Changing Dimensions)
07Proficiency in Python or another scripting language is required
08Experience with data orchestration tools (e.g., Airflow, Dagster) for building and managing data workflows
09Resourceful, results-oriented, and autonomous, with a proven track record of owning the full lifecycle of analytical projects
10Excellent verbal and written communication and stakeholder management skills