This company is a fast-growing technology company building advanced AI-driven solutions powered by deep learning and large-scale data processing. The organization develops proprietary predictive models that enable real-time, data-driven decision-making across complex markets.

We are looking for an experienced Data Engineer who wants to play a key role in designing and scaling robust data infrastructure in a production environment.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines (batch and streaming).

• Optimize distributed data processes for performance and efficiency.

• Ensure data quality, consistency, and reliability across workflows.

• Work with cloud-native architectures and modern data platforms.

• Implement and manage orchestration frameworks (e.g., Airflow, Dagster).

• Collaborate closely with data science, research, and business teams.

• Develop and maintain CI/CD processes and data testing frameworks.

• Monitor, troubleshoot, and continuously improve pipeline performance and stability.

Requirements

• 4+ years of professional experience with Python and SQL.

• Proven experience building and maintaining data pipelines (ETL/ELT).

• Hands-on experience with orchestration tools such as Airflow, Dagster, or similar.

• Strong experience with distributed data processing frameworks (Spark, Dask, Beam).

• Experience with at least one major cloud platform (AWS, GCP, or Azure); BigQuery is a plus.

• Solid understanding of Docker/Kubernetes and CI/CD pipelines.

• Strong software engineering fundamentals (data structures, algorithms, testing, code quality).

• Experience with data testing and monitoring tools (pytest, Great Expectations, observability tools).

Nice to Have

• Experience with dbt, ClickHouse, or Ray.

• Familiarity with Python data libraries (pandas, NumPy, Apache Arrow, Jinja).

• Background in functional programming.