Position: Sr. Data Engineer
Company: Svitla Systems
Category: Python
Experience required: 5y
English level: intermediate

8+ years of experience in SQL and Python development, including ETL Development and building Star/Snowflake Schemas in a data warehouse;
Proficiency in writing complex SQL and ETL processes;
Expertise in AWS Kinesis, Firehose, Kafka, Flink, Airflow, Docker, DMS, Lambda, and Spark;
Experience with ETL and BI/Reporting tools like Matillion, Looker, Periscope, QuickSights;
Expertise in Data Governance and Management;
Knowledge of REST-based API;
Expertise in model development lifecycle: feature engineering, model development and deployment, orchestration, feedback, and monitoring;
Experience in developing ETL scripts based on technical specifications/data design documents and source to target mappings.
xperience with ETL tools;
Experience with Snowflake, Redshift, and PostgreSQL;
Knowledge of AWS, GCP, or Azure;
Expertise in Git, Dockerization and continuous Integration frameworks, etc.;
Experience of working in a Scrum/Agile environment.
Support the design and development of data strategy to build high-performance systems, focusing on data quality and integrity, system integration validation, including database/backend testing and ETL;
Define the high-level data and ETL strategy;
Work closely with the Product team, QA Engineers, SMEs, determining appropriate data sources for building custom data models;
Learn and contribute to the design of data management and any future transformation of the data environment landscape;
Contribute to the creation of a scalable automation framework with a focus on data integrity, configurability, low complexity, and test case reuse;
Drive the development of data products in collaboration with data analysts;
Design the data models for optimal storage and retrieval, meeting critical product and business requirements;
Build advanced monitoring and alerting systems;
Integrate third-party systems, using their APIs to pull and provide insights on the data;
Review and interpret business requirements and translate them into data models for catering to ad-hoc reporting capabilities.