Data Engineer

Apply cloud and big-data engineering skills to build, test, and maintain data pipelines and data-product components using tools such as Spark, dbt, Airflow, or equivalent cloud-native services. Execute ingestion, transformation, and loading jobs that reliably move data from operational systems, IoT devices, or clinical sources into data platforms at agreed SLAs. Troubleshoot pipeline failures, data-quality anomalies, and performance bottlenecks, diagnosing root causes and implementing fixes without service disruption. Validate that implemented pipelines meet data contracts, schema standards, and access-control policies defined by data architects and governance teams. Optimize pipeline performance and cloud infrastructure costs by profiling job runtimes, partitioning strategies, and resource configurations. Document pipeline designs, data lineage, and operational runbooks in shared repositories accessible to data consumers and support teams. Coach less-experience colleagues on data-engineering patterns, testing practices, and cloud-platform tooling used in the assigned domain. Reference Code:| 894554 | | --- Contact Us + 022-25311234 Required - Bachelor's degree in Computer Science, Data Engineering, Information Technology, or a related field, 6+ years of experience Proficiency in SQL, Python, PySpark, Spark, or another Programming language, such as Java, Scala or R Proven experience building highly scalable, performant, resilient, and cost-optimized data pipelines Experience with modern data processing frameworks, such as Snowflake, Databricks, or AWS SageMaker Unified Studio Hands-on experience with core AWS services, including Amazon S3, IAM, Lake Formation, AWS Glue, Lambda, and related services required to build, operate, and secure end-to-end data solutions Strong understanding of relational databases and modern lakehouse table formats (Apache Iceberg, Delta Lake) Expertise in design, build, maintain and document conceptual, logical, and physical data models Sound understanding of data governance principles, access control, data cataloging, lineage, encryption, auditability, and compliance requirements. Ability to implement secure cloud environments and role-based access controls using IAM and Lake Formation. Familiarity with the concept of Retrieval-Augmented Generation (RAG) data pipelines Familiarity with data architecture patterns, such as data hub, mesh, or fabric Experience with RESTful APIs and data integration Familiarit with the Data Product concept and hands-on implications Relevant certifications (e.g., GCP Certified, AWS Certified, Azure Certified) Demonstrated experience with Terraform for infrastructure automation, together with Git, GitHub, GitHub Actions, and CI/CD practices. Experience with GxP and computerized system validation