Data Engineer
First seen by Alion on Sep 2, 2026.
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
Skills
Data Engineering, Data Science, Python, SQL, Machine Learning, Big Data, ETL, Power BI, Tableau, Apache Spark