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