Senior Data Engineer Databricks and Snowflake
First seen by Alion on Nov 5, 2026.
Senior Data Engineer Databricks and Snowflake
Exp- 7+ years
Rate: 30 LPA
Location: Remote
Time zone: ET (US Eastern time)
Shift: Night Shift
Type: Fulltime
Main tech requirements: Databricks, Snowflake, Spark, AWS, RDS, Oracle, MSSQL, domain of histopathology
Responsibilities:
Design, build, and maintain scalable data pipelines for acquiring, integrating, and managing data from diverse data generation sources and systems (e.g., lab systems, MES, clinical supply, quality systems, external partners).
Create and optimize data flows for structured and unstructured data using Python (PySpark), R, SQL, Databricks, Snowflake, and other modern engineering tools.
Develop and maintain specific data repositories, implementing enterprise-level data models, and creating new models as needed.
Enable AI/ML readiness by ensuring data is well-structured, versioned, traceable, and semantically aligned with enterprise data standards.
What we expect:
Bachelor’s degree in Engineering, Data Science, Life Sciences, Computer Science, or related field; advanced degree preferred.
6+ years of experience in data engineering, including data modeling and database design, preferably in a scientific, manufacturing, or healthcare environment.
Proficiency with Python, R, SQL, and cloud-based architectures (AWS services, Snowflake, Databricks, Redshift) along with Spark and dbt.
Proficiency in RDS
Expertise in ETL and DWH.
Experience with NoSQL and graph databases.
English language proficiency of B2+
Strong analytical, problem-solving, and stakeholder-management skills, with the ability to translate discussions into actionable requirements.
Ability to drive multiple exciting projects simultaneously with strong organizational skills and adaptability.
Nice to have:
Experience working with the histopathology domain
Experience with regulated or standards-driven data environments, such as CDISC, HL7, FHIR, OMOP, DICOM, or manufacturing/quality data standards.
Familiarity with high dimensional data (e.g., imaging, sensor data, etc).
Experience with principles connecting to or feeding MLOps and model deployment workflows.
Knowledge of manufacturing systems (MES), laboratory information systems, or industrial data systems.
Exposure to knowledge graph or ontology-driven architectures.
Skills
Data modeling, Python, React.js, SQL, AWS services, NOSQL Databases, CDISC, HL7, FHIR, OMOP, DICOM