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zorba ai

GCP Data Engineer

zorba ai
Chennai, India
8-10 Years
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  • Posted 5 days ago
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Job Description

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

More Info

Job Type:
Permanent Job
Industry:
Other
Function:
Data Engineering
Employment Type:
Full time

About Company

zorba ai

Job ID: 147804543

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Last Updated: 24-05-2026 05:35:17 PM
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