Data Engineer - ML & AI

As a Sr. Data Engineer, you will be responsible for designing, developing, and maintaining scalable software applications and solutions to address complex business needs, with a focus on building robust data foundations for data repositories, integrations, and reporting. This role ensures system performance and availability while minimizing downtime through automation and proactive incident management. The ideal candidate will collaborate with product owners, architects, and engineers to design a next-generation metrics engine and implement a new data governance capability, including metadata and reference data management for analytics. Additional responsibilities include designing interfaces, workflows, and data models, conducting data mapping across systems, optimizing data migration, and deploying integrations in development and production environments, ensuring compliance and operational excellence.

This position is perfect for a collaborative, detail-oriented professional passionate about enhancing clinical operations through advanced data engineering and integrations.

Roles & Responsibilities:

Collaborate closely with product owners, data architects, business SMEs, and engineers to develop and deliver high-quality solutions, enhancing and maintaining integrations across clinical systems.

Design and architect the next-generation metrics engine on modern infrastructure to support operational analytics leveraging cloud technologies.

Design and implement a new data governance capability incorporating essential features like metadata and reference data management for analytics.

Take ownership of complex software projects from conception to deployment, managing scope, risk, and timelines.

Utilize rapid prototyping skills to quickly translate concepts into working solutions and code.

Leverage modern AI/ML technologies to enable predictive analytics, NLP/NLQ capabilities, and enhance the overall data analytics process.

Analyze functional and technical requirements of applications, translating them into software architecture and design specifications.

Develop and execute unit tests, integration tests, and other testing strategies to ensure software quality and reliability.

Integrate systems and platforms to ensure seamless data flow, functionality, and interoperability.

Provide ongoing support and maintenance for applications, ensuring smooth and efficient operation.

Collaborate on building advanced analytics capabilities to empower data-driven decision-making and operational insights.

Provide technical guidance and mentorship to junior developers, fostering team growth and skill development.

Basic Qualifications and Experience:

Doctorate Degree OR

Master's degree with 4 - 6 years of experience in Computer Science, IT, or a related field OR

Bachelor's degree with 6 - 8 years of experience in Computer Science, IT, or a related field OR

Diploma with 10 - 12 years of experience in Computer Science, IT, or a related field OR

Diploma with 14 - 18 years of experience in Computer Science, IT, or a related field.

Functional Skills:

Must-Have Skills:

Strong understanding of cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).

Hands-on experience writing SQL using any RDBMS (Redshift, Postgres, MySQL, Teradata, Oracle, etc.).

Hands-on experience with big data technologies and platforms, such as Databricks, Apache Spark (PySpark, SparkSQL), workflow orchestration, and performance tuning on big data processing.

Excellent communication skills and the ability to interface with senior leadership and Architects with confidence and clarity.

Good-to-Have Skills:

Experience with software DevOps CI/CD tools, such as Git, Jenkins, Linux, and Shell Script.

Experience with Spark, Hive, Kafka, Kinesis, Spark Streaming, and Airflow.

Experience with data flow pipelines to extract, transform, and load data from various data sources in various forms, including custom ETL pipelines.

Experience working in an agile environment (i.e., user stories, iterative development, etc.).

Soft Skills:

Excellent analytical and troubleshooting skills.

Strong verbal and written communication skills.

Ability to work effectively with global, virtual teams.

High degree of initiative and self-motivation.

Ability to manage multiple priorities successfully.

Team-oriented, with a focus on achieving team goals.

Strong presentation and public speaking skills.