zorba ai
Website:
zorbaconsulting.in
Job details:
Key Required Skills
- Programming & Data Processing: Advanced SQL, Python; Scala/Java for Spark/Flink (Go is a plus)
- Cloud Data Platforms: Hands-on with BigQuery, Snowflake, Redshift, Synapse/Databricks SQL; strong DW vs MPP understanding
- Data Modelling: Dimensional modelling, Data Vault 2.0, SCDs, schema evolution
- Streaming: Kafka/Pub/Sub/Kinesis, Spark Streaming/Flink; schema management and processing reliability
- Orchestration & ELT: Airflow/Composer, dbt or similar tools
- CI/CD & Platform Engineering: Git workflows, automated pipelines, Terraform/CloudFormation, Docker/Kubernetes
- Data Quality & Governance: Data contracts, testing frameworks, lineage/catalog tools
- BI & Semantics: KPI/metric modelling, semantic layers, enterprise BI exposure
- AI Readiness: Feature engineering, ML/GenAI data patterns, knowledge layers
- Security & Compliance: IAM, encryption, masking/tokenization, auditability
Key Responsibilities
- Build reusable pipeline frameworks (batch & streaming) with standard templates
- Design analytics-ready data models (star/snowflake, Data Vault 2.0)
- Optimize cloud data warehouse performance and cost
- Develop robust streaming pipelines with SLA-driven delivery
- Implement data quality frameworks and governance controls
- Enable metadata-driven engineering and lineage tracking
- Establish semantic layers for BI and self-service analytics
- Prepare AI-ready data foundations (feature datasets, knowledge models)
- Ensure observability, monitoring, and FinOps optimization
- Drive engineering excellence through CI/CD, IaC, and best practices
Ideal Candidate Profile – Must Have Skill SetMandatory Technical Skills
- Advanced SQL and strong Python programming
- Hands-on experience with Spark using Scala or Java
- Strong expertise in at least one cloud data warehouse/platform:
- Snowflake
- BigQuery
- Redshift
- Synapse
- Databricks SQL
- Strong understanding of Data Warehousing and MPP architecture
Mandatory Data Engineering Experience
- Dimensional Modelling (Star/Snowflake Schema)
- Data Vault 2.0
- Slowly Changing Dimensions (SCDs)
- Batch and Streaming pipeline development
Mandatory Streaming Skills
- Kafka / Pub-Sub / Kinesis
- Spark Streaming or Apache Flink
- Real-time data processing and schema management
Mandatory Orchestration & ELT Skills
- Airflow / Cloud Composer
- dbt or equivalent ELT framework
Mandatory DevOps & Platform Skills
- Git-based CI/CD workflows
- Terraform or CloudFormation
- Docker & Kubernetes
Mandatory Governance & Quality Skills
- Data quality frameworks and testing
- Metadata, lineage, and governance implementation
- Security concepts:
- IAM
- Encryption
- Masking/tokenization
Mandatory BI & Analytics Exposure
- Semantic layer and KPI/metric modelling
- Enterprise BI and self-service analytics exposure
AI/ML Readiness (Must Have)
- Feature engineering concepts
- AI/ML or GenAI data preparation exposure
- Knowledge layer/data foundation understanding
Ideal Experience Range
- 8+ years overall experience in Data Engineering
- Strong experience in enterprise cloud data platform implementations
- Experience building scalable, reusable data framework
Ideal Experience Range
- 8+ years overall experience in Data Engineering
- Strong experience in enterprise cloud data platform implementations
- Experience building scalable, reusable data frameworks
- Key Required Skills
- Programming & Data Processing: Advanced SQL, Python; Scala/Java for Spark/Flink (Go is a plus)
- Cloud Data Platforms: Hands-on with BigQuery, Snowflake, Redshift, Synapse/Databricks SQL; strong DW vs MPP understanding
- Data Modelling: Dimensional modelling, Data Vault 2.0, SCDs, schema evolution
- Streaming: Kafka/Pub/Sub/Kinesis, Spark Streaming/Flink; schema management and processing reliability
- Orchestration & ELT: Airflow/Composer, dbt or similar tools
- CI/CD & Platform Engineering: Git workflows, automated pipelines, Terraform/CloudFormation, Docker/Kubernetes
- Data Quality & Governance: Data contracts, testing frameworks, lineage/catalog tools
- BI & Semantics: KPI/metric modelling, semantic layers, enterprise BI exposure
- AI Readiness: Feature engineering, ML/GenAI data patterns, knowledge layers
- Security & Compliance: IAM, encryption, masking/tokenization, auditability
Key Responsibilities
- Build reusable pipeline frameworks (batch & streaming) with standard templates
- Design analytics-ready data models (star/snowflake, Data Vault 2.0)
- Optimize cloud data warehouse performance and cost
- Develop robust streaming pipelines with SLA-driven delivery
- Implement data quality frameworks and governance controls
- Enable metadata-driven engineering and lineage tracking
- Establish semantic layers for BI and self-service analytics
- Prepare AI-ready data foundations (feature datasets, knowledge models)
- Ensure observability, monitoring, and FinOps optimization
- Drive engineering excellence through CI/CD, IaC, and best practices
Ideal Candidate Profile – Must Have Skill SetMandatory Technical Skills
- Advanced SQL and strong Python programming
- Hands-on experience with Spark using Scala or Java
- Strong expertise in at least one cloud data warehouse/platform:
- Snowflake
- BigQuery
- Redshift
- Synapse
- Databricks SQL
- Strong understanding of Data Warehousing and MPP architecture
Mandatory Data Engineering Experience
- Dimensional Modelling (Star/Snowflake Schema)
- Data Vault 2.0
- Slowly Changing Dimensions (SCDs)
- Batch and Streaming pipeline development
Mandatory Streaming Skills
- Kafka / Pub-Sub / Kinesis
- Spark Streaming or Apache Flink
- Real-time data processing and schema management
Mandatory Orchestration & ELT Skills
- Airflow / Cloud Composer
- dbt or equivalent ELT framework
Mandatory DevOps & Platform Skills
- Git-based CI/CD workflows
- Terraform or CloudFormation
- Docker & Kubernetes
Mandatory Governance & Quality Skills
- Data quality frameworks and testing
- Metadata, lineage, and governance implementation
- Security concepts:
- IAM
- Encryption
- Masking/tokenization
Mandatory BI & Analytics Exposure
- Semantic layer and KPI/metric modelling
- Enterprise BI and self-service analytics exposure
AI/ML Readiness (Must Have)
- Feature engineering concepts
- AI/ML or GenAI data preparation exposure
- Knowledge layer/data foundation understanding
Ideal Experience Range
- 8+ years overall experience in Data Engineering
- Strong experience in enterprise cloud data platform implementations
- Experience building scalable, reusable data frameworks
- Key Required Skills
- Programming & Data Processing: Advanced SQL, Python; Scala/Java for Spark/Flink (Go is a plus)
- Cloud Data Platforms: Hands-on with BigQuery, Snowflake, Redshift, Synapse/Databricks SQL; strong DW vs MPP understanding
- Data Modelling: Dimensional modelling, Data Vault 2.0, SCDs, schema evolution
- Streaming: Kafka/Pub/Sub/Kinesis, Spark Streaming/Flink; schema management and processing reliability
- Orchestration & ELT: Airflow/Composer, dbt or similar tools
- CI/CD & Platform Engineering: Git workflows, automated pipelines, Terraform/CloudFormation, Docker/Kubernetes
- Data Quality & Governance: Data contracts, testing frameworks, lineage/catalog tools
- BI & Semantics: KPI/metric modelling, semantic layers, enterprise BI exposure
- AI Readiness: Feature engineering, ML/GenAI data patterns, knowledge layers
- Security & Compliance: IAM, encryption, masking/tokenization, auditability
Key Responsibilities
- Build reusable pipeline frameworks (batch & streaming) with standard templates
- Design analytics-ready data models (star/snowflake, Data Vault 2.0)
- Optimize cloud data warehouse performance and cost
- Develop robust streaming pipelines with SLA-driven delivery
- Implement data quality frameworks and governance controls
- Enable metadata-driven engineering and lineage tracking
- Establish semantic layers for BI and self-service analytics
- Prepare AI-ready data foundations (feature datasets, knowledge models)
- Ensure observability, monitoring, and FinOps optimization
- Drive engineering excellence through CI/CD, IaC, and best practices
Ideal Experience Range
- 8+ years overall experience in Data Engineering
- Strong experience in enterprise cloud data platform implementations
- Experience building scalable, reusable data frameworks
Skills: python,java,scala,advance sql
Click on Apply to know more.