Back
Tier-1 brand, common Data Engineer title, Hyderabad metro, and broad required skillset drive high competition.
Strong data engineering skills are transferable, but biotech R&D and enterprise data fabric experience increase industry specificity.
Explicit 3–8 year bands, required Databricks/PySpark/AWS skills and biotech R&D preference create high shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own design, development, and optimization of scalable ETL/ELT pipelines and data integration frameworks across enterprise R&D data.
Implement and optimize big data processing solutions using technologies like Apache Spark and AWS ensuring high availability and cost efficiency.
Collaborate with cross-functional teams to develop metadata-driven architectures, data governance, security, and enterprise-wide data fabric for seamless analytics access.
Bachelor’s degree with 5 to 8+ years relevant experience OR Master’s degree with 3 to 4+ years relevant experience in Computer Science, IT or related field.
Hands-on experience with Databricks, PySpark, SparkSQL, Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
Experience with enterprise data architectures such as Data Fabric or Data Mesh.
Work Experience Required: 3 to 8+ years depending on degree; AWS Certified Data Engineer and Databricks Certificate preferred; Scaled Agile SAFe certification preferred.
Deep functional knowledge of Biotech or Pharma R&D data environments with enterprise-wide data engineering experience.
Expertise in big data processing, distributed computing, and metadata management frameworks within a regulated industry context.
Experienced in automated deployment (CI/CD), data security compliance, and aligning data strategies with enterprise goals in a scaled agile environment.