Data Engineer Support

Company Profile

Flentas helps Startups, SMEs & Enterprises leverage the full potential of the cloud. We are AWS Advanced Consulting Partners with certified cloud engineers who support organizations through every stage of their Digital Transformation journey, helping them attain maximum ROI. Our services include Cloud Managed Services, Cloud-Native Application Development, Cloud Migration, DevOps Implementation, Cloud Governance Automation, Big Data Consulting, Resource Augmentation, IoT, and Generative AI solutions. About the Role

Flentas is looking for a highly skilled Data Engineer with 7-10 years of experience in designing, building, and optimizing scalable data platforms and pipelines. The ideal candidate will have strong expertise in Python, PySpark, Databricks, AWS Cloud, SQL, and modern data engineering practices , with a proven track record of delivering enterprise-grade data solutions. This role offers the opportunity to work on cutting-edge cloud and data transformation projects for leading customers across industries. Key Responsibilities

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. Required Skills & Experience 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 .