Senior Data Engineer

• We are seeking a Data Engineer with programming foundations—especially in Python—to design, build, and operate reliable data pipelines and analytics infrastructure. You will work closely with analytics teams to transform raw data into trusted, high‑quality datasets and performance‑optimized tables in ClickHouse and PostgresSQL, orchestrated via Apache Airflow.
  
  What You’ll Do
• Design, build, and maintain scalable ETL/ELT pipelines in Python, orchestrated with Airflow (DAGs, operators, sensors, schedules, SLAs, backfills).
• Contribute to platform improvements (frameworks, libraries, and best practices) to accelerate data delivery.
• Model, load, and optimize datasets in ClickHouse and PostgreSQL (partitioning, indexing, materialized views where appropriate, query performance tuning).
• Implement data quality checks, validation, and monitoring (e.g., unit/integration tests for pipelines, schema/contracts, alerts).
• Manage and improve CI/CD for data workflows

Required Qualifications:

• 1+ years of experience as a Data Engineer (or similar role) building production‑grade data pipelines.
• Strong Python expertise: clean code, packaging, type hints, testing (pytest), and performance optimization; familiarity with Django or FastAPI is a plus.
• Hands‑on experience with Apache Airflow: Authoring TaskFlow‑style DAGs, custom operators/sensors, scheduling, retries, backfills, SLAs. Operational familiarity: UI monitoring, logs, pools/queues, variable/connection management.
• Proficiency with ClickHouse: Table engines, partitioning, primary keys, compression. Query optimization, materialized views, distributed tables, ingestion strategies.
• Strong SQL skills and PostgreSQL experience: Schema design and normalization/denormalization, indexing, query analysis, performance tuning.
• Comfort with Linux, shell scripting, and basic networking concepts.

Additional Qualifications:

• Experience with streaming ecosystems (Kafka), CDC, or near‑real‑time pipelines.
• Experience with Apache Spark for distributed data processing.
• Familiarity with dbt, Great Expectations, or similar tooling for transformations and data quality.
• Containerization with Docker; Kubernetes is a plus.
• Experience with Jenkins for CI/CD pipeline orchestration.