DATA ENGINEERS – PHYGITAL DRIVE
- Location
- Hyderabad
- Workplace
- On-site
- Employment
- Full-time
- Compensation
- ₹1300k – ₹1400k
About this role
Summary
We are seeking a skilled Data Engineer to spearhead our Phygital Drive initiative within the IT Services sector. This pivotal role focuses on designing, building, and maintaining robust data infrastructure that bridges digital and physical operational needs. The successful candidate will be instrumental in architecting scalable data solutions, ensuring seamless data flow, and enabling advanced analytics to drive strategic business decisions across our organization.
Responsibilities
- Architect and implement scalable data lake and lakehouse solutions utilizing platforms such as AWS, Azure, or GCP.
- Develop, optimize, and manage complex ETL and ELT processes to ensure high-quality data ingestion and transformation.
- Design and construct efficient data pipelines using tools like Apache Airflow and frameworks including Apache Spark or PySpark.
- Collaborate with cross-functional teams to define data requirements and deliver high-performance data models in Snowflake or Databricks.
- Write clean, efficient, and maintainable code in Python, SQL, or Scala to support various data engineering tasks.
- Monitor and troubleshoot data infrastructure to guarantee reliability, security, and optimal performance of data assets.
Requirements
Requirements:
- Possess a work experience level between 2 to 8 years in data engineering or related technical fields.
- Demonstrate proficiency with major cloud data platforms including AWS, Azure, or Google Cloud Platform (GCP).
- Show hands-on expertise with orchestration tools like Apache Airflow and big data processing engines such as Apache Spark.
- Have a strong command of data warehousing and lakehouse technologies, specifically Snowflake or Databricks.
- Be proficient in coding languages essential for data manipulation, including Python, SQL, and Scala.
- Exhibit a solid understanding of Data Lake architecture principles and experience in developing end-to-end data pipelines.
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