Job Description :

Immediate joiners or less than 30 days notice period.

Key Responsibilities :

- Design, build, and optimize scalable data pipelines and ETL workflows.

- Develop and maintain data models, data marts, data warehouses, and analytical datasets.

- Perform exploratory data analysis and identify meaningful trends, patterns, and insights.

- Develop, train, validate, and monitor predictive and classification models.

- Apply statistical analysis, hypothesis testing, experimental design, and A/B testing.

- Perform data preprocessing, feature engineering, model validation, and performance evaluation.

- Translate analytical findings into clear and actionable business recommendations.

- Create data visualizations and reports for technical and non-technical stakeholders.

- Implement data quality checks, validation rules, monitoring, logging, and alerting.

- Automate data workflows and ensure reliable and timely data availability.

- Collaborate with data engineering and AI teams to operationalize models and analytical solutions.

- Evaluate and select suitable AI foundation models where applicable.

Technical Skills :

- Strong programming skills in Python or R.

- Advanced SQL and experience with SQL databases.

- Strong knowledge of statistics, machine learning, and data mining.

- Hands-on experience with data preprocessing, feature engineering, and model validation.

- Data visualization using Power BI, Tableau, Matplotlib, or Seaborn.

- Experience with big data technologies such as Apache Spark or Hadoop.

- Hands-on experience with cloud data platforms such as Azure, AWS, or GCP.

- Knowledge of cloud ML platforms such as Azure ML, AWS SageMaker, or GCP AI/ML services.

- Experience with data warehousing platforms such as Snowflake, Redshift, or BigQuery.

Data Quality & Testing :

- Develop and execute data validation and data quality test cases.

- Perform unit and integration testing for data pipelines.

- Validate data accuracy, completeness, consistency, and integrity.

- Identify data anomalies and coordinate with engineering teams for resolution.

- Maintain testing documentation, results, and logs.

Cloud & Platform Experience :

- Experience with Azure Data Factory, AWS Glue, GCP BigQuery, or similar cloud data services.

- Knowledge of orchestration tools such as Apache Airflow, Azure Data Factory, or AWS Glue.

- Understanding of Docker, Kubernetes, or containerized environments.

- Familiarity with DevOps practices and CI/CD for data pipelines.

- Experience with monitoring and operational support for data workflows.

Preferred Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field.

- 8 to 12 years of relevant experience in data engineering, data science, machine learning, or related domains.

- Strong hands-on experience with Python, SQL, and distributed data systems.

- Ability to communicate complex technical concepts clearly to non-technical stakeholders.

- Experience deploying and maintaining data or machine learning solutions in production environments.

Key Skills :

- Python - SQL - Data Engineering - ETL - Data Pipelines - Machine Learning - Statistics - EDA - Feature Engineering - Data Quality - Apache Spark - Hadoop - Azure/AWS/GCP - Airflow/ADF/Glue - Power BI/Tableau - Snowflake/Redshift/BigQuery - CI/CD - Docker - Kubernetes