Senior Data Engineer

Задачи: — Design, build, and maintain data infrastructure and pipelines spanning both batch and real-time / streaming workloads, contributing to architectural decisions along the way — Build and maintain scalable, efficient, and reliable ETL/ELT pipelines using languages and frameworks such as Python, SQL, Spark, Flink, Beam, or equivalents — Work on real-time or near-real-time data solutions (e.g. CDC, streaming, micro-batch) for use cases that require timely data — Partner with data scientists, ML engineers, analysts, and product teams to understand data requirements, define SLAs, and deliver coherent data products that others can self-serve — Establish data quality, validation, observability, and monitoring frameworks (data auditing, alerting, anomaly detection, data lineage) — Investigate and resolve complex production issues: root cause analysis, performance bottlenecks, data integrity, fault tolerance — Document data flows, data dictionaries, architecture patterns, and operational runbooks Требования: — 5+ years of experience in data engineering (or similar) roles — Strong experience in ETL/ELT pipeline design, implementation, and optimization — Deep expertise in Python and SQL writing production-quality, maintainable, testable code — Experience with large-scale data warehouses (e.g. Databricks, BigQuery, Snowflake) — Solid grounding in software engineering fundamentals, data structures, and systems thinking — Hands-on experience in data modeling (dimensional modeling, normalization, schema design) — Experience building systems with real-time or streaming data (e.g. Kafka, Kinesis, Flink, Spark Streaming), and familiarity with CDC frameworks — Experience with orchestration / workflow frameworks (e.g. Airflow) — Familiarity with data governance, lineage, metadata, cataloging, and data quality practices