Design, build, and optimize scalable ETL/ELT data pipelines using PySpark and Python. Model and tune MongoDB for large datasets, develop data ingestion and validation frameworks, integrate REST APIs, monitor and troubleshoot pipelines, and collaborate with analysts and data scientists while applying data governance and CI/CD practices.
Job Title: Data Engineer (MongoDB, PySpark & Python)
Experience
Experience
5–8 Years
As per business requirement
We are looking for an experienced Data Engineer with strong expertise in MongoDB, PySpark, and Python to design, develop, and optimize scalable data pipelines. The ideal candidate should have experience working with large datasets, NoSQL databases, distributed data processing, and cloud-based data platforms.
- Design, build, and maintain scalable ETL/ELT data pipelines using PySpark and Python.
- Develop data ingestion frameworks to process structured, semi-structured, and unstructured data.
- Work extensively with MongoDB for data modeling, querying, indexing, aggregation, and performance optimization.
- Optimize Spark jobs for high-performance processing of large datasets.
- Build reusable data transformation and validation frameworks.
- Develop REST API integrations and automate data ingestion using Python.
- Monitor, troubleshoot, and optimize data pipelines for reliability and performance.
- Collaborate with business analysts, data scientists, and application teams to deliver data solutions.
- Implement data quality, governance, and security best practices.
- Participate in code reviews and follow CI/CD and Agile development practices.
- Strong experience in Python programming.
- Hands-on experience with PySpark and Spark SQL.
- Strong knowledge of MongoDB, including:
- CRUD Operations
- Aggregation Framework
- Indexing
- Replication
- Sharding
- Performance Tuning
- CRUD Operations
- Good understanding of data structures and algorithms.
- Experience in developing ETL/ELT pipelines.
- Strong SQL skills.
- Experience with Git version control.
- Knowledge of Linux/Unix commands.
- Experience working with JSON, XML, and Parquet data formats.
- Experience with Databricks.
- Experience with cloud platforms such as Azure, AWS, or GCP.
- Knowledge of Apache Kafka or other streaming technologies.
- Experience with orchestration tools such as Apache Airflow.
- Understanding of Delta Lake and Lakehouse architecture.
- Familiarity with CI/CD pipelines.
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