AI SummaryVerified by Aipplify AI
The vacancy is strong in task clarity and requirements but lacks compensation details.
AI quality score6.5 / 10
Check Match โ Just drop your CV
See your fit for Data Engineer in seconds.
Overview
CINIMEX is looking for a Senior Data Engineer to develop ETL pipelines and optimize data processes using Apache Spark, ClickHouse, and more. Flexible work format in Moscow, Nizhny Novgorod, or St. Petersburg.
Responsibilities
- โขDevelopment of ETL pipelines (Apache Spark on Java, HDFS (parquets), ClickHouse, Hive, Greenplum);
- โขDevelopment of data marts in Greenplum and ClickHouse;
- โขOrchestration of ETL processes;
- โขOptimization of ETL processes (batching, retries, SLA control);
- โขInteraction with BI developers and DevOps for timely data delivery to Superset.
Conditions
- โขWork in an accredited IT company;
- โขWork format: office/hybrid (Moscow/Nizhny Novgorod/Saint Petersburg);
- โขFlexible start time: from 08:00 to 11:00 (MSK);
- โขHealth insurance including dental, telemedicine, and travel insurance;
- โขAbility to take sick leave without a sick note: 7 days a year;
- โขGradual adaptation from HR and line manager;
- โขReview and development: we evaluate results, discuss career goals, and build an individual development plan;
- โขIT conferences, courses, training, and certifications funded by the company;
- โขCorporate English;
- โขPersonal brand development: preparing speakers for conference presentations, assisting in article writing;
- โขInternal meetups, online/offline activities (quizzes, board game nights, sports events).
Requirements
- โขDeep expert experience with Apache Spark for 4-5 years (job optimization, working with wide and narrow dependencies, memory and shuffle operations tuning);
- โขExperience with ClickHouse;
- โขExperience with Hadoop (HDFS, Hive);
- โขExperience in designing and developing data streams, loading and processing algorithms;
- โขExperience in optimizing ETL pipelines and SQL code;
- โขAdvanced SQL knowledge;
- โขWillingness to learn Java for Spark usage.
Loading similar jobs...