Senior Data Engineer

Role: Senior Data Engineer (Semantic Layer, Knowledge Graphs and AI Enablement)
Location: Remote
Duration: [6 to 12 months, with possible extension]
Pay Rate: $75 to $85
Overview: We are looking for a Senior Data Engineer who is equally comfortable writing production code and shaping data architecture. In this hands-on technical leadership role, you will design and build scalable data solutions while guiding engineering best practices across the team. You will work closely with data scientists, AI engineers, and business stakeholders to deliver high quality, connected, AI ready data.

Key Responsibilities:
Data Engineering and Architecture

• Design, build, and maintain scalable data pipelines for structured and unstructured data from internal and external sources.
• Write production quality SQL and Python and contribute actively to development.
• Ensure data solutions are scalable, reliable, secure, and performant.

Data Modelling, Semantic Layer and Knowledge Graphs

• Design conceptual, logical, and physical data models and standardization frameworks across heterogeneous data sources.
• Develop semantic models, ontologies, taxonomies, and knowledge graphs that represent business and scientific entities and their relationships.
• Implement entity resolution and graph-based data integration to create reusable, connected data assets for analytics and AI.
• Establish standards for data modelling, metadata, ontology management, and semantic interoperability.

Data Products and AI Enablement

• Build reusable data products and APIs that support analytics, AI/ML, LLM, and agentic AI use cases.
• Partner with data scientists and AI engineers to deliver high quality, AI ready data and contextual data services.

Data Quality, Metadata and Governance

• Profile and map disparate datasets, identify data quality issues, and drive data standardization.
• Champion metadata, lineage, governance, security, and privacy practices.

Cloud, Orchestration and Observability

• Design and operate cloud based, containerized, and orchestrated data workloads.
• Implement monitoring, alerting, and event driven frameworks to ensure reliability and observability.

Collaboration and Continuous Improvement

• Partner with business, product, domain experts, and technical teams to translate business needs into scalable data solutions.
• Evaluate and introduce technologies and engineering practices that improve the team’s capabilities and delivery.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.
• 7+ years of experience in data engineering or similar roles, including leading projects or initiatives.
• Proven track record designing and scaling cloud based data platforms, preferably on AWS.
• Strong proficiency in SQL, Python, and advanced data modeling, with hands on experience designing conceptual, logical, and physical models for complex, heterogeneous datasets.
• Experience leading architecture decisions and implementing best practices.
• Strong understanding of data quality, integration, transformation, and governance.
• Excellent communication skills, with the ability to influence both technical and non technical stakeholders.

Preferred Qualifications:

• Hands on experience designing and implementing semantic data models, semantic layers, ontologies, taxonomies, or knowledge graphs (for example Neo4j, RDF, OWL, SKOS, SPARQL).
• Strong understanding of entity modelling, relationships, metadata, entity resolution, and data standardization across disparate sources.
• Experience translating complex business or domain concepts into reusable data models and semantic structures that support analytics, AI/ML, and enterprise data products.
• Hands on experience with AWS data services such as Glue, Redshift, S3, Lambda, and Athena.
• Experience supporting AI/ML data pipelines and workflows.
• Familiarity with metadata management and data governance frameworks.
• Experience in healthcare, life sciences, or related domains.
• Experience working in Agile environments.

Key Skills: Python, SQL, AWS (Glue, Redshift, S3, Lambda, Athena), data modelling, knowledge graphs, ontologies, taxonomies, semantic layer, entity resolution, metadata management, data governance, data quality, orchestration, containerization, observability, AI/ML data pipelines.

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