Data Engineer

SmartLunch is a platform that provides employees with access to tasty and healthy meals, demonstrating that companies care about people not only professionally but also daily. The company is building a new analytical domain and is looking for a technical specialist to improve data quality, architecture, and flow within the organization.

• Minimum 4 years of experience in data engineering.
• Strong knowledge of SQL and practical experience with dbt.
• Experience with cloud data warehouses such as Snowflake or BigQuery.
• Experience designing and implementing data models and ETL/ELT pipelines.
• Understanding of event analysis and event tracking.
• Experience with analytical/BI tools (e.g., Hex, Looker, Tableau) for data provisioning and optimization.
• Ability to independently lead technical tasks end-to-end from concept to production.
• Comfortable working in a dynamic product/technology environment.
• Emphasis on code readability, automation, and high-quality solutions.

Nice to have:

• Knowledge of Python for automation and data integration.
• Experience with task orchestration platforms (e.g., Airflow, Dagster, Prefect).
• Experience in product or technology companies during scaling phase.
• Education in computer science, mathematics, physics, or related fields.
• Experience with data ingestion/integration tools like Airbyte, Fivetran, Stitch, Meltano, or custom API integrations.

• Developing and maintaining data architecture and models based on dbt and cloud data warehouses.
• Building standards for data engineering, modeling best practices, and maintaining code cleanliness and architecture.
• Designing, implementing, and optimizing ETL/ELT data pipelines.
• Collaborating with analysts, product, and cross-functional teams to provide high-quality data structures for analytics and reporting.
• Building data model layers for analytics, reporting, KPIs, and self-service BI.
• Maintaining Data Governance, data quality, consistency, and reliability.
• Evaluating current technology setup and recommending further development of data tools and infrastructure.
• Designing, developing, and monitoring data ingestion processes from source systems, APIs, databases, CRM/finance/marketing tools, and product applications to data warehouses.