TL;DR

Middle Data Engineer: Building and operating reliable ELT/ETL pipelines on Snowflake and Airflow with an accent on data ingestion, transformation, and orchestration. Focus on designing modular SQL/Python transformations, ensuring pipeline reliability, and tuning performance and cost in Snowflake.

What you will do

Ingest data from RDBMS, APIs, and files into Snowflake using batch, incremental, and CDC methods.
Build modular SQL and Python transformations handling semi-structured JSON data.
Orchestrate Airflow DAGs with dependencies, retries, backfills, SLAs, monitoring, and alerting.
Ensure idempotent re-runs and backfills; maintain runbooks and perform root cause analysis for incidents.
Optimize Snowflake performance and cost through warehouse sizing, pruning, and clustering.
Collaborate with BI and Analytics teams to refine dataset definitions and SLAs.

Requirements

2–4 years experience in production ETL/ELT pipelines with strong SQL and Python skills.
Hands-on experience with Snowflake features including Streams, Tasks, Time Travel, and JSON handling.
Proficiency in Airflow for reliable DAG management, monitoring, and alert routing.
Knowledge of data warehousing/modeling (Kimball/3NF), schema evolution, and API integrations.
Experience with Git-based CI/CD, clear written English, and privacy/GDPR basics.

Nice to have

Familiarity with iGaming domain concepts and regulatory awareness.
Interest or experience in AI and automation, including Snowflake Cortex and semantic search.
Exposure to cloud storage (GCS, S3, ADLS), Terraform, Docker, and BI tools (Tableau, Looker, Power BI).

Culture & Benefits

Direct cooperation with a successful, long-term, and growing project.
Flexible work arrangements.
20 days of vacation.
Competitive salary.
Supportive HR team.