Data Engineer (Elasticsearch + Datawarehousing)

Join a dynamic engineering environment where you’ll design and build scalable data solutions that power impactful client projects.
You’ll work across Elasticsearch, data warehouses, data lakes, APIs, databases, and cloud platforms to solve complex data challenges.
The role offers end-to-end ownership, from gathering requirements and designing data architectures to implementation, testing, and optimization.
You’ll work with both structured and unstructured data, creating reliable pipelines and unified datasets for analytics and business use cases.
Your work will directly contribute to high-performance, secure, and maintainable data infrastructure.
You’ll collaborate closely with product managers, engineers, testers, and other stakeholders in an Agile environment.
The position also provides opportunities to explore modern data engineering technologies while continuously developing your technical expertise.

• Bachelor’s degree in Computer Science, Engineering, or a related technical discipline.
• At least 3 years of professional experience in data engineering or a closely related role.
• Strong hands-on proficiency with Elasticsearch and Python.
• Experience working with relational databases such as MySQL or PostgreSQL and NoSQL technologies such as MongoDB.
• Strong understanding of data warehousing and data lakehouse principles, database architecture, ORM concepts, and data processing.
• Experience with technologies and frameworks such as Flask, Databricks, Pandas, Spark, PySpark, or similar data engineering tools.
• Familiarity with machine learning and data analysis libraries such as Scikit-learn or OpenCV is advantageous.
• Experience using Java to develop or enhance backend systems, particularly where integration with Elasticsearch and databases is involved.
• Ability to develop APIs, microservices, and automation scripts for data and backend workflows.
• Familiarity with tools and libraries including logging, requests, subprocess, regex, and pytest.
• Experience with the ELK stack, Redis, and distributed task queues is a plus.
• Strong understanding of concurrent and parallel processing concepts.
• Familiarity with at least one major cloud data engineering ecosystem, such as AWS, Azure, or GCP, with the ability to adapt quickly to different ETL/ELT tools.
• Experience with Git and collaborative version-control workflows.
• Comfortable working with Linux environments and creating shell scripts.
• Solid understanding of software engineering principles, design patterns, testing practices, and maintainable development.
• Strong analytical and problem-solving abilities with excellent attention to detail.
• Effective written and verbal communication skills and the ability to collaborate across multidisciplinary teams.
• Adaptability, curiosity, and willingness to learn new technologies as project requirements evolve.