Associate - Data Engineering

Job Summary:
We are seeking a talented and experienced Data Engineer to join our team. The ideal candidate will be responsible for designing, building, and maintaining scalable data pipelines and systems to support analytics and data-driven decision-making. This role requires expertise in data processing, data modeling, and big data technologies.

Key Responsibilities:

• Design and develop datapipelines to collect, transform, and load data into datalakes and datawarehouses.
• Optimize ETLworkflows to ensure data accuracy, reliability, and scalability.
• Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements.
• Implement and manage cloud−baseddataplatforms (e.g., AWS, Azure, or GoogleCloudPlatform).
• Develop datamodels to support analytics and reporting.
• Monitor and troubleshoot data systems to ensure high performance and minimal downtime.
• Ensure data quality and security through governance best practices.
• Document workflows, processes, and architecture to facilitate collaboration and scalability.
• Stay updated with emerging data engineering technologies and trends.

Required Skills and Qualifications:

• Strong proficiency in SQL and Python for data processing and transformation.
• Hands-on experience with bigdatatechnologies like ApacheSpark, Hadoop, or Kafka.
• Knowledge of datawarehousingconcepts and tools such as Snowflake, BigQuery, or Redshift.
• Experience with workfloworchestrationtools like ApacheAirflow or Prefect.
• Familiarity with cloudplatforms (AWS, Azure, GCP) and their data services.
• Understanding of datagovernance, security, and compliance best practices.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.

Preferred Qualifications:

• Certification in cloudplatforms (AWS, Azure, or GCP).
• Experience with NoSQLdatabases like MongoDB, Cassandra, or DynamoDB.
• Familiarity with DevOpspractices and tools like Docker, Kubernetes, and Terraform.
• Exposure to machinelearningpipelines and tools like MLflow or Kubeflow.
• Knowledge of datavisualizationtools like PowerBI, Tableau, or Looker.