Data Analyst - Python/SQL

Job Description :

We are seeking a skilled Data Analyst to support our platform, powering operational intelligence across airports and similar sectors. The ideal candidate will have experience working with time-series datasets and operational information to uncover trends, anomalies, and actionable insights. This role will work closely with data engineers, ML teams, and domain experts to turn raw data into meaningful intelligence for business and operations stakeholders.

Responsibilities :

- Analyze time series and sensor data from various sources

- Develop and maintain dashboards, reports, and visualizations to communicate key metrics and trends.

- Correlate data from multiple systems (vision, weather, flight schedules, etc) to provide holistic insights.

- Collaborate with AI/ML teams to support model validation and interpret AI-driven alerts (e. g., anomalies, intrusion detection).

- Prepare and clean datasets for analysis and modeling; ensure data quality and consistency.

- Work with stakeholders to understand reporting needs and deliver business-oriented outputs.

Requirements :

- Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Engineering, or a related field.

- 5+ years of experience in a data analyst role, ideally in a technical/industrial domain.

- Strong SQL skills and proficiency with BI/reporting tools (e. g., Power BI, Tableau, Grafana).

- Hands-on experience analyzing structured and semi-structured data (JSON, CSV, time-series).

- Proficiency in Python or R for data manipulation and exploratory analysis.

- Understanding of time-series databases or streaming data (e. g., InfluxDB, Kafka, Kinesis).

- Solid grasp of statistical analysis and anomaly detection methods.

- Experience working with data from industrial systems or large-scale physical infrastructure.

- Domain experience in airports, smart infrastructure, transportation, or logistics.

- Familiarity with data platforms (Snowflake, BigQuery, Custom-built using open-source).

- Exposure to tools like Airflow, Jupyter Notebooks, and data quality frameworks.

- Basic understanding of AI/ML workflows and data preparation requirements.