Assoc Director- Platform & Data Engineer
Reads like a Data Leadership role: running a data team.
Hyderabad (Office)
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and optimize batch, streaming, CDC, and event-driven data pipelines
Develop and optimize Databricks, Spark, Delta Lake, and Lakehouse solutions
Design Snowflake ingestion, transformation, and governance solutions
Build reusable integration frameworks, APIs, and CDC processes across enterprise systems
Build RAG pipelines, vector retrieval, and agentic workflows for enterprise AI
From the employer’s posting
Design, build, and optimize scalable batch, streaming, CDC, and event-driven data pipelines
Lead hands-on development and optimization of Databricks, Apache Spark, Delta Lake, and Lakehouse solutions
Design and implement Snowflake data engineering solutions, including ingestion, transformation, governance, orchestration, and secure data consumption patterns.
Develop reusable integration frameworks, APIs, CDC processes, and connectivity patterns across enterprise applications, SaaS platforms, governance tools, ERP, and CRM systems.
Build AI-ready data foundations, including RAG pipelines, vector retrieval, context engineering, and agentic workflows
What you’ll bring
All qualificationsCore experience
- Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Information Technology, or a related discipline, or equivalent practical experience.
- 12+ years of experience in software engineering and/or data engineering with proven delivery of large-scale production data platforms.
- Strong experience with Apache Spark (PySpark), distributed data processing, performance tuning, and troubleshooting.
- Deep knowledge of AWS cloud services, cloud-native data architectures, security, observability, Infrastructure as Code, and automation.
- Experience implementing CI/CD pipelines, automated testing, monitoring, governance, data quality, security controls, and operational support practices.
- Strong understanding of enterprise integration patterns, APIs, SaaS integrations, metadata management, lineage, data governance, and access control frameworks.
Qualification wording
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Information Technology, or a related discipline, or equivalent practical experience.
12+ years of experience in software engineering and/or data engineering with proven delivery of large-scale production data platforms.
Strong experience with Apache Spark (PySpark), distributed data processing, performance tuning, and troubleshooting.
Deep knowledge of AWS cloud services, cloud-native data architectures, security, observability, Infrastructure as Code, and automation.
Experience implementing CI/CD pipelines, automated testing, monitoring, governance, data quality, security controls, and operational support practices.
Strong understanding of enterprise integration patterns, APIs, SaaS integrations, metadata management, lineage, data governance, and access control frameworks.
Education & alternatives
Minimum Requirements - Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Information Technology, or a related discipline, or equivalent practical experience. - 12+ years of experience in software engineering and/or data engineering with proven delivery of large-scale production data platforms.
Tools in this posting
- Python
- SQL
- AWS
- Databricks
- Delta Lake
- Snowflake
- Spark
- PySpark
Source — Tool mentions in context
Job Description Summary This role leads the design and delivery of enterprise data platforms and AI-ready solutions using AWS, Databricks, Snowflake, Spark, and Python. It is a highly hands-on technical position focused on building scalable data pipelines, integrations, data products, RAG and agentic AI capabilities, while ensuring strong governance, security, reliability, automation, and performance Job Description
Major Accountabilities - Design, build, and optimize scalable batch, streaming, CDC, and event-driven data pipelines using modern data engineering best practices and production-grade Python and SQL. - Architect secure, resilient, high-performance data platforms and translate business and technical requirements into scalable cloud-based solutions.
- 12+ years of experience in software engineering and/or data engineering with proven delivery of large-scale production data platforms. - Advanced hands-on expertise in Python, SQL, data modeling, ETL/ELT, CDC, batch, streaming, and event-driven architectures. - Strong experience with Apache Spark (PySpark), distributed data processing, performance tuning, and troubleshooting.
- Design and implement Snowflake data engineering solutions, including ingestion, transformation, governance, orchestration, and secure data consumption patterns. - Engineer enterprise data platforms on AWS, leveraging cloud-native services to deliver secure, automated, observable, and highly available data solutions. - Develop reusable integration frameworks, APIs, CDC processes, and connectivity patterns across enterprise applications, SaaS platforms, governance tools, ERP, and CRM systems.
- Proven hands-on experience with Databricks, Delta Lake, and Snowflake in enterprise-scale environments. - Deep knowledge of AWS cloud services, cloud-native data architectures, security, observability, Infrastructure as Code, and automation. - Experience implementing CI/CD pipelines, automated testing, monitoring, governance, data quality, security controls, and operational support practices.
- Architect secure, resilient, high-performance data platforms and translate business and technical requirements into scalable cloud-based solutions. - Lead hands-on development and optimization of Databricks, Apache Spark, Delta Lake, and Lakehouse solutions, ensuring reliability, performance, and cost efficiency. - Design and implement Snowflake data engineering solutions, including ingestion, transformation, governance, orchestration, and secure data consumption patterns.
- Strong experience with Apache Spark (PySpark), distributed data processing, performance tuning, and troubleshooting. - Proven hands-on experience with Databricks, Delta Lake, and Snowflake in enterprise-scale environments. - Deep knowledge of AWS cloud services, cloud-native data architectures, security, observability, Infrastructure as Code, and automation.
Skills Desired Databricks Platform, Snowflake (Platform)
- Lead hands-on development and optimization of Databricks, Apache Spark, Delta Lake, and Lakehouse solutions, ensuring reliability, performance, and cost efficiency. - Design and implement Snowflake data engineering solutions, including ingestion, transformation, governance, orchestration, and secure data consumption patterns. - Engineer enterprise data platforms on AWS, leveraging cloud-native services to deliver secure, automated, observable, and highly available data solutions.
- Advanced hands-on expertise in Python, SQL, data modeling, ETL/ELT, CDC, batch, streaming, and event-driven architectures. - Strong experience with Apache Spark (PySpark), distributed data processing, performance tuning, and troubleshooting. - Proven hands-on experience with Databricks, Delta Lake, and Snowflake in enterprise-scale environments.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 20, 2026 · 39 days ago
- Recorded sightings
- 12
- Last seen by us
- Oct 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
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Job description
Job Description Summary
This role leads the design and delivery of enterprise data platforms and AI-ready solutions using AWS, Databricks, Snowflake, Spark, and Python. It is a highly hands-on technical position focused on building scalable data pipelines, integrations, data products, RAG and agentic AI capabilities, while ensuring strong governance, security, reliability, automation, and performance
Job Description
Major Accountabilities
- Design, build, and optimize scalable batch, streaming, CDC, and event-driven data pipelines using modern data engineering best practices and production-grade Python and SQL.
- Architect secure, resilient, high-performance data platforms and translate business and technical requirements into scalable cloud-based solutions.
- Lead hands-on development and optimization of Databricks, Apache Spark, Delta Lake, and Lakehouse solutions, ensuring reliability, performance, and cost efficiency.
- Design and implement Snowflake data engineering solutions, including ingestion, transformation, governance, orchestration, and secure data consumption patterns.
- Engineer enterprise data platforms on AWS, leveraging cloud-native services to deliver secure, automated, observable, and highly available data solutions.
- Develop reusable integration frameworks, APIs, CDC processes, and connectivity patterns across enterprise applications, SaaS platforms, governance tools, ERP, and CRM systems.
- Build AI-ready data foundations, including RAG pipelines, vector retrieval, context engineering, and agentic workflows, enabling enterprise AI and GenAI use cases.
- Drive engineering excellence through CI/CD, Infrastructure as Code, security-by-design, observability, governance, mentoring, and end-to-end ownership of data products and platform capabilities.
Minimum Requirements
- Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Information Technology, or a related discipline, or equivalent practical experience.
- 12+ years of experience in software engineering and/or data engineering with proven delivery of large-scale production data platforms.
- Advanced hands-on expertise in Python, SQL, data modeling, ETL/ELT, CDC, batch, streaming, and event-driven architectures.
- Strong experience with Apache Spark (PySpark), distributed data processing, performance tuning, and troubleshooting.
- Proven hands-on experience with Databricks, Delta Lake, and Snowflake in enterprise-scale environments.
- Deep knowledge of AWS cloud services, cloud-native data architectures, security, observability, Infrastructure as Code, and automation.
- Experience implementing CI/CD pipelines, automated testing, monitoring, governance, data quality, security controls, and operational support practices.
- Practical exposure to Generative AI technologies, including RAG architectures, embeddings, vector search, LLM integration, context engineering, and agentic workflows.
- Strong understanding of enterprise integration patterns, APIs, SaaS integrations, metadata management, lineage, data governance, and access control frameworks.
- Excellent stakeholder management, communication, mentoring, problem-solving, and delivery leadership skills with a highly hands-on engineering mindset.
Why Novartis?
Our purpose is to reimagine medicine to improve and extend people’s lives and our vision is to become the most valued and trusted medicines company in the world. How can we achieve this? With our people. It is our associates that drive us each day to reach our ambitions. Be a part of this mission and join us!
Learn more here:
https://www.novartis.com/about/strategy/people-and-culture
Commitment to Diversity and Inclusion:
Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.
Join our Novartis Network: If this role is not suitable to your experience or career goals but you wish to stay connected to hear more about Novartis and our career opportunities, join the Novartis Network here:
https://talentnetwork.novartis.com/network
Skills Desired
Databricks Platform, Snowflake (Platform)Source details
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- Location & working pattern
Hyderabad (Office)
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