Project description

Our Lithuanian client is focused on building modern, cloud-based, and ML-enabled data platforms. They work with many Baltic / Nordic leading companies and have various projects to work on. Mostly, re-building of companies’ data stack to enable new capabilities like data science, self-service business intelligence, scalable data warehouse, or machine learning. They currently looking for experienced Data Engineers in Ukraine to join their international team (15 in Sweden, less than 10 in Lithuania, 2 Belarussian and 2 Ukrainians).

Required skills

— Experience in building, improving, and monitoring data pipelines (ETL/ELT) for big data environments;

— Knowledge of technologies Microsoft Azure Data Factory, Databricks, Machine Learning services, Power BI, Logic Apps;

— Knowledge of data infrastructure monitoring and support;

— Have an agile mindset and believes in CI/CD way of working;

— English: Intermediate+ (daily communication).

As a plus

— Experience with Jupyter Notebook, Hadoop eco-system, Python, Java, SQL, Docker/Kubernetes, Spark, Grafana, ELK stack;

— Previous work experiences as a data engineer, database administrator, data analyst, or similar;

— Knowledge of technologies Microsoft SQL (+HA), Microsoft SQL SSRS, SSAS, SSIS;

— Certifications of data-related competencies (DBA, OCP, and other).

We offer

— Possibility to work remotely or in the office;

— Competitive above-average salary;

— Challenging tasks and professional growth;

— Paid vacations, public holidays, and sick leaves;

— Professional education, sports, and healthcare reimbursement (50% compensation);

— IT Cluster/Club bonus program.

Responsibilities

— Provide, implement and configure tools & data infrastructure — for data analysts, data scientists, and business stakeholders to efficiently work with large amounts of data, discover unique insights, and present them in the best possible way.

— Shape and customize our solution in modern data technologies. Working with stakeholders, users, and other teams to build, innovate, continuously improve both our data platform, and our ways of working;

— Support customer data infrastructure and solutions delivered according to SLA.