Must have

• SQL

• Data analytics

• dbt

• AWS

• Python

• Power BI

• English (B2)

Nice to have

• Snowflake

• AWS Glue

Requirements description

• 5+ years of experience in Data Analytics.
• 1+ years of experience with dbt
• Advanced SQL skills with extensive hands-on experience.
• Experience leading data modelling activities and building models from scratch.
• Familiarity with Metabase, Power BI, or other BI tools.
• Proven track record of translating data insights into business actions and taking full ownership.
• Proficiency with Python or R for data analysis and automation
• Previous experience with version control and continuous integration to a code base.
• Understanding of ETL/ELT processes and data pipeline orchestration.
• Experience with scheduling/report automation (e.g., using cron, AWS Lambda, or Step Functions).
• Knowledge of data security and governance within AWS.
• Train and provide technical mentorship to other data analysts/engineers
• Adaptable and able to thrive in a fast-paced, dynamic work setting.
• Strong organizational skills with keen attention to detail.
• Excellent communication, analytical and interpersonal skills.
• English - Upper intermediate or Advanced

Offer description

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

Your responsibilities

1. Consolidate data from various domains and construct interactive dashboards
2. Data modelling using dimensional (Kimall) approach
3. Conduct ad hoc analyses as required with extensively usage of SQL
4. Identify and address data quality issues (in reports)
5. Work with our current data stack: Snowflake, dbt, AWS services
6. Collaborate with teams from the Data Engineering, Analytics, Data Science teams to establish and maintain consistent use of business definitions in a source of truth tables, and recommend continuous improvement strategies.

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