Data Engineer

About Company

Grid Dynamics (Nasdaq : GDYN) is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernization. Grid Dynamics achieves high speed-to-market, quality, and efficiency by using technology accelerators, an agile delivery culture, and its pool of global engineering talent. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the US, UK, Netherlands, Mexico, and Central and Eastern Europe.

Job Description : Role : Data Engineer

Years of experience : 4+ Years

Basic Qualifications

4+ years of combined experience in data engineering, database architecture, and DevOps roles.

Proven production experience with Neo4j or another graph database

Expert-level Python development skills, with strong knowledge of distributed data-processing tools

Track record of designing and operating incremental / CI-CD data pipelines.

Hands-on knowledge of GenAI concepts : embeddings, vector databases / stores, Retrieval-Augmented Generation (RAG), prompt engineering, and agent frameworks.

Experience with version control systems such as Git.

Strong problem-solving skills and attention to detail.

“Get-it-done” mindset : comfortable diving into new technologies, quickly learning what’s needed, and shipping reliable solutions.

Qualifications :

4+ years of professional experience in data engineering.

Strong proficiency in SQL , Pandas / Polars / Fireducks and Python.

Hands-on experience with Apache Spark and Kafka.

Familiarity with ETL processes and data pipeline orchestration.

Strong problem-solving and analytical skills.

Experience with version control systems such as Git.

Hands on experience with GenAI tools like chatGPT, CoPilot, Aider, Cline etc.

Need strong foundation on python , pydantic ,pyUnit and pytest

Hands-on experience with DevOps practices, including Continuous Integration, Continuous Deployment, and Test Automation

Hands-on experience with UI automation frameworks like Selenium or Robot Framework.

Data Exploratory Analysis

Familiarity with ETL processes and data pipeline orchestration

Strong proficiency in SQL , Pandas / Polars / Fireducks and Python.