Data Engineer

Posted 23 Days Ago
Be an Early Applicant
Hiring Remotely in Kyiv, Gorod-Geroy Kiyev, UKR
In-Office or Remote
Mid level
Software
The Role
Design and develop data platforms and pipelines for analytics, implement workflows using distributed frameworks, and integrate data from various sources.
Summary Generated by Built In
Company Description

Are you passionate about building scalable, high-performance data solutions? We are looking for a Data Engineer to join our team and help design and develop robust data platforms that power analytics, reporting, and data-driven decision-making.

Why join us? You will work on challenging projects, collaborate with talented experts, and have access to professional growth opportunities, a supportive environment, and competitive benefits.

Job Description

  • Design and develop scalable, reliable data platforms and pipelines for analytics and operations
  • Implement data processing workflows using distributed frameworks (Apache Spark/pySpark, Databricks)
  • Evolve the organization’s cloud data platform leveraging Azure technologies (Data Factory, Synapse, Event Hub, Azure Data Lake Storage)
  • Model data to support analytics, reporting, and downstream consumption
  • Integrate and process data from multiple sources ensuring quality and consistency
  • Monitor and optimize pipeline performance, ensuring reliability and scalability
  • Collaborate with analysts, scientists, and engineers to translate requirements into solutions
  • Improve data engineering standards, testing, and operational reliability
  • Evaluate and introduce new tools and technologies in the data engineering ecosystem

Qualifications

  • 3+ years of hands-on experience in data engineering or building data processing platforms
  • Strong SQL skills and solid Python experience for pipeline development
  • Experience with distributed processing frameworks (Apache Spark/pySpark, Databricks preferred)
  • Practical experience designing and implementing pipelines in cloud environments (Azure preferred)
  • Experience with production-scale analytical systems and data modeling
  • Understanding of ETL/ELT design, dimensional modeling, and data warehousing principles
  • Experience with modern data lake architectures and formats (Parquet, JSON)
  • Familiarity with workflow orchestration tools (Airflow)
  • Experience with database development and optimization
  • Ability to design scalable solutions with minimal supervision
  • Strong collaboration and communication skills
  • Professional proficiency in English

Top Skills

Airflow
Spark
Azure Data Factory
Azure Data Lake Storage
Azure Event Hub
Azure Synapse
Databricks
JSON
Parquet
Pyspark
Python
SQL
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The Company
New York, New York
1,516 Employees

What We Do

Sigma Software Group, an award-winning and trusted IT partner, has been serving customers for over 21 years, providing comprehensive IT solutions to various businesses, ranging from startups to established software product houses. As one of Europe's substantial IT consultancies, it brings together a dedicated workforce of over 2,100 professionals in 40 offices across 19 countries. With a diverse client base, including more than 300 enterprises, including Fortune 500 stalwarts, Sigma Software Group is a preferred choice for developing solutions that help businesses create cutting-edge products while meeting their unique needs. Sigma Software Group operates as a dynamic ecosystem of tech companies, offering 25 ready-to-implement innovative products and 40+ value-added services. Furthermore, Sigma Software Group is committed to fostering innovation through initiatives such as the Sigma Software Labs business incubator, Sigma Software University, the SID Venture Partners VC Fund, UA Tech Network, Techosystem, the European Business Association, and other collaborative efforts. Since 2015, Sigma Software Group has consistently earned recognition on the IAOP's prestigious World's Top 100 Outsourcing list. The company's accomplishments have also been acknowledged by prominent global media outlets such as Forbes, CNBC, The Times, and Reuters

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