All fields marked * are required
Key Responsibilities
Data Integration & Architecture
● Design and implement scalable enterprise data integration architecture across ERP, EPM, QMS, CPT, Quote Management, and other enterprise applications.
● Develop robust ETL/ELT pipelines for high-volume, multi-source data ingestion and transformation.
● Build reusable data transformation frameworks to standardize and normalize data across multiple business units.
● Establish master data management (MDM) processes for product hierarchies, SKU mappings, and cross-system reconciliation.
● Develop automated data quality validation, monitoring, and exception handling frameworks.
● Implement metadata management, data lineage, and governance standards to ensure traceability and compliance.
API Development & Enterprise Integration
● Design, develop, and maintain high-performance RESTful APIs and GraphQL services for enterprise applications and AI model consumption.
● Build secure API layers enabling seamless communication between forecasting models and business applications.
● Develop middleware and integration services connecting Finance, Operations, Supply Chain, Sales, and AI platforms.
● Implement API gateways, authentication, authorization, monitoring, and lifecycle management.
● Optimize API performance to support high-volume, low-latency enterprise workloads.
● Produce comprehensive API documentation and developer enablement materials.
Real-Time Data Processing
● Build and optimize streaming data pipelines for near real-time ingestion and processing.
● Develop infrastructure supporting anomaly detection for pricing, forecasting deviations, derates, and operational exceptions.
● Create automated alerting mechanisms and recommendation engines for proactive business decision-making.
● Enable both scheduled and on-demand data refreshes while maintaining sub-24-hour processing cycles.
● Support real-time dashboards and analytics for planning and forecasting.
System Integration & Data Governance
● Develop enterprise connectors using modern integration patterns for ERP, EPM, QMS, CPT, CRM, and other business systems.
● Implement reconciliation processes and validation checkpoints to ensure data consistency across platforms.
● Establish data governance policies, security controls, role-based access, and audit capabilities.
● Integrate operational and financial datasets to support forecasting, profitability, margin, and planning analytics.
● Maintain version control and audit trails for data pipelines, business rules, and forecasting assumptions.
Performance, Monitoring & Optimization
● Monitor integration pipelines, API performance, and overall platform health.
● Continuously optimize data pipelines for scalability, reliability, and cost efficiency.
● Troubleshoot production issues and perform root cause analysis.
● Document technical architecture, integration patterns, APIs, and data models.
● Collaborate with analytics, AI/ML, platform engineering, and business teams to improve platform performance.
Required Qualifications
Education
● Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline.
● Master's degree is preferred.
Experience
● 7+ years of experience in Integration Development, Data Engineering, or Enterprise Integration.
● 4+ years designing and implementing enterprise ETL/ELT platforms.
● Hands-on experience integrating ERP, EPM, Manufacturing, or Supply Chain systems.
● Proven experience building scalable, production-grade integration platforms supporting high-volume enterprise data.
● Experience with enterprise API development and microservices architecture.
● Demonstrated success delivering highly available production systems with 99%+ uptime.
Technical Skills
Data Integration & ETL
● Apache Airflow
● Talend
● Informatica
● dbt
● Custom ETL frameworks
● SQL
● Python
● Java
● Scala
Data Platforms
● Snowflake
● Amazon Redshift
● Google BigQuery
● Azure Synapse Analytics
● Data quality and validation frameworks
Streaming & Real-Time Processing
● Apache Kafka
● Spark Streaming
● AWS Kinesis
● Event-driven architecture
● Real-time data processing frameworks
API & Application Development
● RESTful APIs
● GraphQL (preferred)
● FastAPI
● Flask
● Spring Boot (preferred)
● API Gateway implementation
● Microservices architecture
● OAuth2, JWT, API Security
Cloud & DevOps
● AWS, Azure, or Google Cloud Platform
● Docker
● Kubernetes
● CI/CD pipelines
● Infrastructure as Code
● Git
● DevOps best practices
Databases
● PostgreSQL
● MongoDB
● Spark ecosystem
● Hadoop ecosystem
● Vector Databases (Pinecone, FAISS, Weaviate, Milvus)
Domain Knowledge
● Supply Chain and Manufacturing systems
● Financial Planning & Analysis (FP&A)
● Forecasting and Demand Planning
● Pricing and Quote-to-Cash processes
● Enterprise Data Governance
● Master Data Management
● Cross-functional enterprise integrations
Soft Skills
● Strong analytical and problem-solving capabilities.
● Excellent communication and stakeholder management skills.
● Ability to explain complex technical concepts to business users.
● Strong ownership with a proactive and solution-oriented mindset.
● Ability to manage multiple priorities in a fast-paced environment.
● Collaborative team player with strong documentation skills.
Preferred Qualifications
● Experience building AI/ML data pipelines and feature engineering workflows.
● Exposure to enterprise forecasting platforms and financial planning solutions.
● Experience implementing real-time anomaly detection systems.
● Knowledge of financial close and accounting processes.
● Cloud certifications (AWS, Azure, or GCP).
● Experience contributing to open-source data engineering projects.
● Familiarity with IATF 16949 or Quality Management Systems.
Nice to Have
● Experience working in manufacturing, automotive, semiconductor, or industrial domains.
● Exposure to AI-driven forecasting and predictive analytics platforms.
● Knowledge of enterprise integration platforms such as MuleSoft, Boomi, or Azure Integration
Services.
● Experience with event-driven architecture and serverless integrations.
● Understanding of DataOps and MLOps best practices
Required
Preferred
All fields marked * are required
Key Responsibilities
Data Integration & Architecture
● Design and implement scalable enterprise data integration architecture across ERP, EPM, QMS, CPT, Quote Management, and other enterprise applications.
● Develop robust ETL/ELT pipelines for high-volume, multi-source data ingestion and transformation.
● Build reusable data transformation frameworks to standardize and normalize data across multiple business units.
● Establish master data management (MDM) processes for product hierarchies, SKU mappings, and cross-system reconciliation.
● Develop automated data quality validation, monitoring, and exception handling frameworks.
● Implement metadata management, data lineage, and governance standards to ensure traceability and compliance.
API Development & Enterprise Integration
● Design, develop, and maintain high-performance RESTful APIs and GraphQL services for enterprise applications and AI model consumption.
● Build secure API layers enabling seamless communication between forecasting models and business applications.
● Develop middleware and integration services connecting Finance, Operations, Supply Chain, Sales, and AI platforms.
● Implement API gateways, authentication, authorization, monitoring, and lifecycle management.
● Optimize API performance to support high-volume, low-latency enterprise workloads.
● Produce comprehensive API documentation and developer enablement materials.
Real-Time Data Processing
● Build and optimize streaming data pipelines for near real-time ingestion and processing.
● Develop infrastructure supporting anomaly detection for pricing, forecasting deviations, derates, and operational exceptions.
● Create automated alerting mechanisms and recommendation engines for proactive business decision-making.
● Enable both scheduled and on-demand data refreshes while maintaining sub-24-hour processing cycles.
● Support real-time dashboards and analytics for planning and forecasting.
System Integration & Data Governance
● Develop enterprise connectors using modern integration patterns for ERP, EPM, QMS, CPT, CRM, and other business systems.
● Implement reconciliation processes and validation checkpoints to ensure data consistency across platforms.
● Establish data governance policies, security controls, role-based access, and audit capabilities.
● Integrate operational and financial datasets to support forecasting, profitability, margin, and planning analytics.
● Maintain version control and audit trails for data pipelines, business rules, and forecasting assumptions.
Performance, Monitoring & Optimization
● Monitor integration pipelines, API performance, and overall platform health.
● Continuously optimize data pipelines for scalability, reliability, and cost efficiency.
● Troubleshoot production issues and perform root cause analysis.
● Document technical architecture, integration patterns, APIs, and data models.
● Collaborate with analytics, AI/ML, platform engineering, and business teams to improve platform performance.
Required Qualifications
Education
● Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline.
● Master's degree is preferred.
Experience
● 7+ years of experience in Integration Development, Data Engineering, or Enterprise Integration.
● 4+ years designing and implementing enterprise ETL/ELT platforms.
● Hands-on experience integrating ERP, EPM, Manufacturing, or Supply Chain systems.
● Proven experience building scalable, production-grade integration platforms supporting high-volume enterprise data.
● Experience with enterprise API development and microservices architecture.
● Demonstrated success delivering highly available production systems with 99%+ uptime.
Technical Skills
Data Integration & ETL
● Apache Airflow
● Talend
● Informatica
● dbt
● Custom ETL frameworks
● SQL
● Python
● Java
● Scala
Data Platforms
● Snowflake
● Amazon Redshift
● Google BigQuery
● Azure Synapse Analytics
● Data quality and validation frameworks
Streaming & Real-Time Processing
● Apache Kafka
● Spark Streaming
● AWS Kinesis
● Event-driven architecture
● Real-time data processing frameworks
API & Application Development
● RESTful APIs
● GraphQL (preferred)
● FastAPI
● Flask
● Spring Boot (preferred)
● API Gateway implementation
● Microservices architecture
● OAuth2, JWT, API Security
Cloud & DevOps
● AWS, Azure, or Google Cloud Platform
● Docker
● Kubernetes
● CI/CD pipelines
● Infrastructure as Code
● Git
● DevOps best practices
Databases
● PostgreSQL
● MongoDB
● Spark ecosystem
● Hadoop ecosystem
● Vector Databases (Pinecone, FAISS, Weaviate, Milvus)
Domain Knowledge
● Supply Chain and Manufacturing systems
● Financial Planning & Analysis (FP&A)
● Forecasting and Demand Planning
● Pricing and Quote-to-Cash processes
● Enterprise Data Governance
● Master Data Management
● Cross-functional enterprise integrations
Soft Skills
● Strong analytical and problem-solving capabilities.
● Excellent communication and stakeholder management skills.
● Ability to explain complex technical concepts to business users.
● Strong ownership with a proactive and solution-oriented mindset.
● Ability to manage multiple priorities in a fast-paced environment.
● Collaborative team player with strong documentation skills.
Preferred Qualifications
● Experience building AI/ML data pipelines and feature engineering workflows.
● Exposure to enterprise forecasting platforms and financial planning solutions.
● Experience implementing real-time anomaly detection systems.
● Knowledge of financial close and accounting processes.
● Cloud certifications (AWS, Azure, or GCP).
● Experience contributing to open-source data engineering projects.
● Familiarity with IATF 16949 or Quality Management Systems.
Nice to Have
● Experience working in manufacturing, automotive, semiconductor, or industrial domains.
● Exposure to AI-driven forecasting and predictive analytics platforms.
● Knowledge of enterprise integration platforms such as MuleSoft, Boomi, or Azure Integration
Services.
● Experience with event-driven architecture and serverless integrations.
● Understanding of DataOps and MLOps best practices
Required
Preferred