Design, develop, and maintain scalable batch and real-time data pipelines.
Build data ingestion frameworks to process structured, semi-structured, and unstructured data from diverse sources.
Develop and optimize data transformation workflows using Python, PySpark, and Databricks .
Implement and manage ETL/ELT processes for large-scale analytics and reporting.
Design and maintain Data Lakes, Lakehouse architectures, and Data Warehouses.
Collaborate with Data Scientists, Business Analysts, Architects, and stakeholders to understand business requirements and translate them into technical solutions.
Ensure data quality, integrity, security, and governance across data platforms.
Optimize Spark jobs, SQL queries, and cloud resources for performance and cost efficiency.
Implement monitoring, logging, and alerting mechanisms for data pipelines.
Contribute to architecture discussions, code reviews, and best practice implementation.

7-10 years of experience in Data Engineering, Big Data, or Cloud Data Platforms.
Strong programming experience in Python .
Hands-on expertise in PySpark and Apache Spark.
Extensive experience with Databricks , Delta Lake, and Lakehouse architecture.
Strong proficiency in SQL and database performance optimization.
Experience building enterprise-scale ETL/ELT pipelines.
Strong hands-on experience with AWS Cloud Platform and cloud-native data engineering solutions.
Experience with data modeling, schema design, and data warehousing concepts.
Experience with workflow orchestration tools such as Apache Airflow or equivalent.
Strong understanding of software engineering principles, version control, and testing methodologies.
Experience using Git .

#J-18808-Ljbffr

Data Engineer

Similar Jobs