Position: Middle Data Engineer
Company: Ciklum
Experience required: 2y
English level: upper

Responsibilities:

- Responsible for managing the pipelining (collection, storage, access), engineering (cleansing, feature creation / selection) and understanding (documentation, exploration) of the data;
- Acquire, create and manage innovative data assets for the team. These assets will draw from various sources – internal and external systems, flat files, databases and external APIs;
- Evaluate user requests for new or re-engineered analytics and reporting solutions to determine their feasibility, time requirements and their compatibility with current systems and architecture standards;
- Active participation in the development of the data lake, data marts and subsequent enhancements, including the creation of new data solutions to support development of analytic capabilities;
- Interact with stakeholders regarding data availability and report format/presentation, to ensure the report provides the information the stakeholders need without compromising the report’s accuracy;
- Deliver new capabilities to the business through agile methods – experiences with Scrum and Jira are a plus.

Requirements:

2+ years of experience coding in SQL, Python, with solid CS fundamentals including data structure and algorithm design;
1+ years contributing to production deployments of large backend data processing and analysis systems as a team lead;
1+ years of experience in cloud data platforms (AWS);
- The ability to design, implement and use effective database structures to ensure data is organised effectively and ready for downstream development of data science and analytics;
- Experience working in a structured environment and desire for good documentation, exceptional delivery, effective organisation, and high-quality communication;
- Experience of Big Data platforms, enabling non-technical users to gain insights into key business metrics;
- Hands on experience with modern data storage systems (e.g. S3, Snowflake, Athena, Glue);
- Feeling comfortable using AWS, Kubernetes, Docker and Airflow
Strong SQL and data profiling skills;
- Hands-on ELT experience using tools like Streams and Tasks or DBT
Familiarity with a traditional BI application such as Power BI, Tableau or Spotfire;
- Knowledge of data types including JSON, XML, YAML is a plus;
- Solid understanding of data warehousing principles, concepts and best practices (e.g. ODS, Data Mart, Data Lakes, Data Vault);
- Strong problem solving and analytic skills