BENGALURU · FULLTIME
SENIOR DATA ENGINEER - Databricks
Happiest Minds Technologies
Bengaluru · onsite · Posted 35d ago
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Undisclosed6–8 yrsfulltimeBengaluru
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Section · 01
About this role
Senior Data Engineer (Databricks)
Mandatory Skills
Spark,Databricks Engineer,Kafka,Stream Processing,SQL Development,Python,Snowflake
Skill to Evaluate
Spark,Databricks Engineer,Snowflake SQL,Kafka,Stream Processing,SQL Development,Python,Snowflake
Experience
6 to 8 Years
Location
Bengaluru
Job Description
Architecture Strategy : End-to-end design of scalable, secure, and highly available data architectures leveraging modern cloud data ecosystems ( Databricks and Snowflake ).
Pipeline Engineering : Architect, optimize, and oversee the deployment of reliable streaming and batch data pipelines (ETL/ELT) to process complex, large-scale datasets.
Cloud Architecture : Architect and deploy scalable enterprise data platform components natively within the AWS ecosystem , ensuring tight integration with core security, IAM, and networking protocols.
API Ingestion Orchestration: Design and implement robust data ingestion frameworks leveraging Databricks APIs and external REST/GraphQL APIs for automated workflows, platform orchestration, and data delivery.
Real-time Processing: Design and implement robust frameworks for real-time data ingestion and processing to solve business-critical, low-latency use cases.
Hybrid Data Modelling: Harmonize "old school" relational data warehousing patterns (Kimball/Inmon, Star/Snowflake schemas) with unstructured/semi-structured modern paradigms.
Technical Leadership: Act as a core problem-solver for complex data bottlenecks, provide technical governance, and mentor engineering teams on data best practices.
AI Integration: Collaborate with Data Science and AI teams to architect data layers that seamlessly support LLMs, Machine Learning pipelines, and advanced analytics solutions.
Education Qualificaiton
B Tech/ M Tech
Job Title
Senior Data Engineer (Databricks)
Roles Responsibilities
Architecture Strategy : End-to-end design of scalable, secure, and highly available data architectures leveraging modern cloud data ecosystems ( Databricks and Snowflake ).
Pipeline Engineering : Architect, optimize, and oversee the deployment of reliable streaming and batch data pipelines (ETL/ELT) to process complex, large-scale datasets.
Cloud Architecture : Architect and deploy scalable enterprise data platform components natively within the AWS ecosystem , ensuring tight integration with core security, IAM, and networking protocols.
API Ingestion Orchestration: Design and implement robust data ingestion frameworks leveraging Databricks APIs and external REST/GraphQL APIs for automated workflows, platform orchestration, and data delivery.
Real-time Processing: Design and implement robust frameworks for real-time data ingestion and processing to solve business-critical, low-latency use cases.
Hybrid Data Modelling: Harmonize "old school" relational data warehousing patterns (Kimball/Inmon, Star/Snowflake schemas) with unstructured/semi-structured modern paradigms.
AI Integration: Collaborate with Data Science and AI teams to architect data layers that seamlessly support LLMs, Machine Learning pipelines, and advanced analytics solutions.
Type of Employment
Contract
Project Details
Architecture Strategy : End-to-end design of scalable, secure, and highly available data architectures leveraging modern cloud data ecosystems ( Databricks and Snowflake ).
Pipeline Engineering : Architect, optimize, and oversee the deployment of reliable streaming and batch data pipelines (ETL/ELT) to process complex, large-scale datasets.
Cloud Architecture : Architect and deploy scalable enterprise data platform components natively within the AWS ecosystem , ensuring tight integration with core security, IAM, and networking protocols.
API Ingestion Orchestration: Design and implement robust data ingestion frameworks leveraging Databricks APIs and external REST/GraphQL APIs for automated workflows, platform orchestration, and data delivery.
Real-time Processing: Design and implement robust frameworks for real-time data ingestion and processing to solve business-critical, low-latency use cases.
Hybrid Data Modelling: Harmonize "old school" relational data warehousing patterns (Kimball/Inmon, Star/Snowflake schemas) with unstructured/semi-structured modern paradigms.
Technical Leadership: Act as a core problem-solver for complex data bottlenecks, provide technical governance, and mentor engineering teams on data best practices.
AI Integration: Collaborate with Data Science and AI teams to architect data layers that seamlessly support LLMs, Machine Learning pipelines, and advanced analytics solutions.
Project Duration
12
Shift Timings
General
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Sourced from naukri
Section · 02
Skills
ArchitectTechnical LeadershiporchestrationNetworking ProtocolsPythonData WarehousingAdvanced AnalyticsData ModelingData ScienceMachine Learning
Section · Company
About Happiest Minds Technologies
Happiest Minds Technologies
IT Services & Consulting
6.5k+
employees
2011
15 years old
Bangalore / Bengaluru, Karnataka
India
About
A technology firm specializing in digital transformation services utilizing advanced technologies to enhance enterprise efficiency and customer experience.
Industries
IT Services & Consulting
Employee ratings
1,441 reviews
3.4/ 5
Culture
3.4
Career growth
2.8
Work-life
3.6
Employees rate it well for
Work Life Balance210Work Culture165Senior Management138Salary104Learning80
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