Lead Consultant -Data Engineer

Inviting applications for the role of Lead Consultant- Data Engineer
Responsibilities
Assist in developing and maintaining data pipelines using PySpark and SparkSQL for data extraction, transformation, and loading.
Collaborate with senior data engineers and cross-functional teams to understand data requirements and assist in designing efficient data models.
Support in implementing data quality checks to ensure data accuracy and integrity.
Assist in optimizing data processing and query performance using Synapse Pipeline and Azure technologies.
Contribute to the design and development of scalable data solutions leveraging Azure services.
Collaborate with data scientists and analysts to assist in their data needs.
Troubleshoot and resolve data-related issues with guidance from senior team members.
Stay updated with the latest industry trends and best practices in data engineering.
Focus Areas
As a Junior Data Engineer, your focus areas will include
Assisting in the development of data pipelines using PySpark and SparkSQL.
Supporting optimization and data processing tasks using Synapse Pipeline and Azure.
Collaborating with senior data engineers to ensure data quality and integrity.
Contributing to the design and development of scalable data solutions using Azure services.
Assisting data scientists and analysts with their data needs.
Staying updated with industry trends and best practices in data engineering.

Qualifications we seek in you
Minimum Qualifications
Bachelors degree in Computer Science, Engineering, or a related field.
Some experience or coursework involving PySpark, SparkSQL, Synapse Pipeline, or Azure.
Basic understanding of data engineering concepts and practices.
Familiarity with databases and SQL.
Key Skill Sets
Basic understanding of PySpark and SparkSQL for data processing and analysis.
Familiarity with Synapse Pipeline and Azure technologies for data engineering tasks.
Basic knowledge of data integration and ETL principles.
Proficiency in programming languages like Python and SQL.
Familiarity with data warehousing concepts and dimensional modeling.
Strong problem-solving and analytical skills.
Effective communication and collaboration abilities.

Preferred Qualifications
Masters degree in Computer Science, Engineering, or a related field.
Certification or additional training in PySpark, SparkSQL, Synapse Pipeline, or Azure.
Familiarity with other big data technologies such as Hadoop, Hive, or Kafka.
Knowledge of data science and machine learning concepts.
Understanding of data governance and security practices.