Collaborate closely with the business teams to understand intricate data requirements in but not limited to bioinformatics, omics, clinical data, and other relevant domains. Translate these requirements into robust and scalable data engineering solutions.
Lead and guide the design, development, and optimization of data pipelines, architectures, and data sets, ensuring efficient data ingestion, transformation, and reliable delivery.
Leverage your extensive experience in data integration technologies, ETL / ELT, and modern data engineering tools, with a strong focus on Informatica/IICS, to create cutting-edge data solutions.
Work hand-in-hand with cross-functional agile teams to architect and implement hybrid-cloud solutions with automated pipelines, ensuring seamless and high-performance data processing.
Drive and oversee the life cycle management of deployed data assets and products, taking charge of new releases, change management, monitoring, and troubleshooting.
Demonstrate your expertise in implementing data warehouse/lake solutions, data mesh architectures, and distributed processing technologies (e.g., Spark, Hadoop, Kafka) for production environments.
Utilize your extensive knowledge of cloud technologies, preferably AWS, to develop and maintain modern cloud-native data platforms with a focus on performance and scalability.
Showcase your advanced proficiency in SQL (preferably in Snowflake) and relational/non-relational databases to optimize complex data queries and manipulations.
Exhibit mastery in programming languages such as Python, Shell scripting, and Scala/Java, leveraging them to develop sophisticated data engineering solutions.
Requirements & Skills:
Degree in Computer Science, Engineering, Mathematics, or a related field. 5-7 years of proven and progressive experience in data engineering, with a strong preference for experience in the life sciences/pharmaceutical industry.
Extensive background in designing, developing, and optimizing data solutions, including data pipelines, architectures, and data sets.
Proven expertise in data integration technologies, ETL / ELT processes, and modern data engineering tools, with an emphasis on Informatica/IICS.
Experience with multimodal data systems and architectures, including batch, near real-time, and streaming data.
Demonstrated success in developing distributed architectures and processing technologies (e.g., Spark, Hadoop, Kafka) for large-scale data processing.
Expertise in developing cloud-native data platforms on AWS, ensuring high performance, scalability, and fault tolerance.
Advanced knowledge of SQL, relational/non-relational databases, and data query optimization.
Proficiency in programming languages such as Python, Shell scripting, and Scala/Java.
Exceptional problem-solving skills and attention to detail, excellent communication, presentation, and interpersonal skills.
