{"id":4263991,"url":"https://alion.io/job/zifo-data-engineer-clinical-2","title":"Data Engineer (Clinical)","company":{"id":686878,"name":"Zifo","domain":"zifo.com","url":"https://alion.io/company/zifo","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":84,"open_postings":8,"ghost_share":0,"stale_share":0.625,"repost_share":0,"time_to_fill_p50_days":32,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mexico City, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon Aurora","optional":false},{"name":"Angular","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Cassandra","optional":false},{"name":"D3.js","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GraphDB","optional":false},{"name":"HBase","optional":false},{"name":"Hugging Face","optional":false},{"name":"Java","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangGraph","optional":false},{"name":"Power BI","optional":false},{"name":"PubMed","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scala","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"TensorFlow","optional":false},{"name":"JavaScript","optional":true},{"name":"LangChain","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-11-13T16:22:14Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T16:22:14Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Does the prospect of data management, cloud, and technology transformation to enable the biopharma industry to data-centric, AI/ML-driven enterprises excite you?\nWe are looking for motivated & self-driven data engineers to join our expanding Scientific Data Foundation team. Zifo serves the pharma & health industry to accelerate the discovery, development, and manufacturing of new drugs, therapies, and vaccines - to save lives faster!\nThis is an exciting opportunity to support pharmaceutical and biotech companies deploying innovative scientific R&D technologies globally. You will enable R&D Digital Transformation, design efficient data pipelines using the latest in Big Data Cloud Infrastructure and engineer new data analytics/visualization capabilities. You will empower our clients to build their future data landscape for data-driven decisions.\nRequirements\nBuild ingestion pipelines for clinical trial protocols, ICF documents, SmPCs, CSRs, and published articles (PubMed, CTIS, ClinicalTrials.gov) - handling PDF parsing, text extraction, and structured data normalization\nDesign and implement data models in Amazon Aurora (relational) and GraphDB (knowledge graph) to represent trial design entities: endpoints, eligibility criteria, study arms, interventions, therapeutic areas, and their relationships\nDevelop embedding and vectorization pipelines to prepare extracted clinical text for RAG-based retrieval in LangGraph agentic workflows - chunking strategies, metadata enrichment, and vector store population\nBuild and maintain ETL/ELT workflows that transform unstructured clinical content into queryable, linked data across both relational and graph stores\nImplement data quality validation specific to clinical data - protocol section classification accuracy, entity extraction completeness, cross-reference integrity (NCT IDs, EudraCT numbers, MeSH terms)\nBuild data serving APIs (Python/FastAPI) that expose curated datasets to the Angular frontend and LangGraph agent layer\nSet up data lineage tracking and audit trails to support regulatory traceability of AI-generated trial design recommendation\nRequired Skills\nResponsibilities:\nIdentify and analyze business needs to determine data related requirements, \nClean, transform, and organize big data for analysis, \nConduct data analysis to identify trends, patterns, and insights, \nDevelop data visualization tools to communicate insights, \nProvide consulting inputs for guidelines, design ideas, standards, & best practices, \nHelp stakeholders to estimate, plan, and prioritize data engineering efforts, \nBuild relationship with clients, internal and partners’ leadership team, and\nRequired Skills:\nExpertise in Python, Spark, Java, Scala for building data pipelines, warehouses, and data processing systems, \nExpertise in relational databases like SQL & NoSQL, \nStrong understanding of NoSQL databases like MongoDB, Cassandra, and HBase, \nFamiliarity with data standards such as FAIR, JSON, OWL and markup languages such YAML \nUnderstanding of AI/ML, data science concepts & cloud architecture \nQualifications:\nBachelor’s or Master’s degree in data engineering / computer science or related field \nExperience with scientific informatics solutions OR background in biological science, chemistry, health sciences OR an experience working in the life or health science industry, \nExperience in cloud computing platforms like AWS, Azure, and Google Cloud Platform (GCP) for deploying and managing data infrastructure in the cloud. \nExperience using version control systems like Git for collaborative code development, \nExperience in data focused solutions such as Snowflake, Databricks, Kafka, Cloudera, Knime, Airflow, etc. \nExperience with AI/ML & data science libraries like Scikit-Learn, TensorFlow, PyTorch, HuggingFace, etc. for preparing data for downstream AI/ML applications, \nFamiliarity with data visualization solution like Tableau, Power Bi and D3.js, \nData engineering related certification from cloud platform like Azure/AWS/Cloudera, and \nCustomer centricity to deliver at the highest standards and elevate customer experience. \nExpereince with Clinical Data is a plus.\nBenefits\nCURIOSITY DRIVEN, SCIENCE FOCUSED, EMPLOYEE BUILT. Our culture is unlike any other, one where we debate, challenge ourselves, and interact with all alike. We are a curious bunch, characterized by our passion to learn and spirit of teamwork. Zifo is a global R&D solutions provider focused on the industries of Pharma, Biotech, Manufacturing QC, Medical Devices, specialty chemicals and other research-based organizations. Our team’s knowledge of science and expertise in technology help Zifo better serve our customers around the globe, including 18 of the Top 20 Biopharma companies.\nWe look for Science - Biotechnology, Pharmaceutical Technology, Biomedical Engineering, Microbiology etc. We possess scientific and technical knowledge and bear professional and personal goals. While we have a “no doors” policy to promote free access within, we do have a tough door to walk in. We search with a two-point agenda - technical competency and cultural adaptability.\nWe offer superior benefits to those outlined by law, a competitive compensation package including accrued vacation, and a healthy Christmas bonus.\nIf you share these sentiments and are prepared for the atypical, then Zifo is your calling!\nZifo Mexico is an equal opportunity employer, and we value diversity at our company. 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