Description

Key Skills: Pyspark, AWS, Azure Databricks, Python, Data Engineer, Ascential Datastage, Asset master data, Azure Data lake, ARIS Database

Roles and Responsibilities:

• 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 within agreed SLAs.
• Troubleshoot pipeline failures, data-quality anomalies and performance bottlenecks, identify root causes and implement 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-experienced colleagues on data-engineering patterns, testing practices and cloud-platform tooling used in the assigned domain.

Skills Required:

• 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 such as Apache Iceberg and Delta Lake.
• Expertise in designing, building, maintaining and documenting 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 Retrieval-Augmented Generation (RAG) data pipelines.
• Familiarity with data architecture patterns such as Data Hub, Data Mesh or Data Fabric.
• Experience with RESTful APIs and data integration.
• Familiarity with the Data Product concept and its hands-on implications.
• Relevant certifications such as GCP Certified, AWS Certified or Azure Certified.
• Demonstrated experience with Terraform for infrastructure automation.
• Experience with Git, GitHub, GitHub Actions and CI/CD practices.
• Experience with GxP and Computerized System Validation (CSV).

Education: Bachelor's degree in Computer Science, Data Engineering, Information Technology or a related field

Key Skills

Education

Any Graduate