Ampera Technologies

Title                                 : Power BI and Microsoft Fabric

Experience                    : 5+ years

Location                         : Remote

Work type                      : Remote

Notice Period               : Immediate

Work Day                      : Mon to Fri

Role Overview

We are seeking an experienced Power BI & Microsoft Fabric Consultant to partner with our BI team for a short-term engagement focused on capability building, governance setup, and platform enablement.

The primary objective of this role is to upskill the team and establish a robust, scalable foundation for Power BI and Microsoft Fabric—covering security, governance, administration, and best practices—so that the team can independently manage and scale the platform post-engagement.

This is a hands-on + coaching role, not just advisory.

Key Responsibilities

1. Power BI & Fabric Enablement

• Conduct structured training sessions and workshops on: 
• Power BI (Pro + Fabric-integrated experiences) 
• Microsoft Fabric (Lakehouse, Warehouse, Dataflows Gen2, Pipelines, etc.) 
• Build foundational understanding of: 
• End-to-end analytics workflows in Fabric 
• Integration between Power BI and Fabric workloads 
• Data Lake will be Snowflake

2. Governance & Security Framework

• Design and implement Power BI & Fabric governance model, including: 
• Workspace strategy (Dev/Test/Prod separation) 
• Naming conventions and standards 
• Content lifecycle management 
• Establish security architecture: 
• Role-based access control (RBAC) 
• Row-Level Security (RLS) / Object-Level Security (OLS) 
• Data access patterns across Fabric and Power BI 
• Define data sharing and access control processes

3 Administration & Platform Setup

• Configure and optimize: 
• Power BI tenant settings 
• Fabric capacity (capacity planning, workload management) 
• Monitoring and usage metrics 
• Set up: 
• Deployment pipelines 
• CI/CD best practices (where applicable) 
• Audit logs and governance controls 

4. Best Practices & Standards

• Define and document: 
• Development standards (data modeling, DAX, report design) 
• Performance optimization guidelines 
• Dataset/reusable semantic model strategy 
• Establish certification and promotion workflows for datasets and reports 

5. Hands-On Implementation

• Work alongside the team to: 
• Build or refactor key dashboards/reports using best practices 
• Set up Fabric artifacts (Lakehouse/Warehouse/Pipelines) 
• Ensure real use cases are implemented, not just theoretical training 

6. Knowledge Transfer & Self-Sufficiency

• Provide: 
• Playbooks, SOPs, and governance documentation 
• Recorded sessions and training materials 
• Mentor team members through: 
• Office hours / Q&A sessions 
• Code and architecture reviews 
• Ensure the team can independently: 
• Manage Fabric capacity 
• Govern Power BI environment 
• Implement secure and scalable solutions 

Expected Outcomes (End of Engagement)

• Fully defined and implemented Power BI & Fabric governance framework
• Configured and optimized Fabric capacity + Power BI tenant
• Established security and access control processes
• Documented standards, playbooks, and operating model
• BI team capable of independent development, administration, and governance

Required Skills & Experience

Must-Have

• 5+ years of experience in Power BI development and administration
• Hands-on experience with Microsoft Fabric (end-to-end)
• Strong expertise in: 
• Power BI governance and tenant administration 
• Fabric capacity management 
• Security models (RLS, RBAC, data access controls) 
• Experience setting up enterprise BI governance frameworks
• Proven track record of training and mentoring teams

Good-to-Have

• Experience with data platforms (Snowflake, Azure, etc.)
• Knowledge of CI/CD for Power BI (DevOps integration)
• Familiarity with data catalog and lineage tools (e.g., Atlan, Alation)
• Understanding of modern architecture

Title                                 : Snowflake Cortex AI Engineer

Experience                    : 5+ years

Location                         : Remote

Work type                      : Remote

Employment Type      : Full Time

Notice Period               : Immediate

Work Day                      : Mon to Fri

About the Role:

We are seeking a highly skilled Snowflake Cortex AI Engineer with 5+ years of experience in Data Engineering, AI, and Snowflake. The ideal candidate will have hands-on expertise in Snowflake Cortex AI, Snowpark, and Generative AI capabilities to build intelligent, scalable, and secure AI-powered data applications. The role involves designing AI-driven solutions, integrating LLM capabilities into enterprise workflows, and collaborating with cross-functional teams to deliver business value.

Key Responsibilities:

• Design, develop, and implement AI-powered solutions using Snowflake Cortex AI.
• Build intelligent data applications leveraging Snowpark, Cortex AI functions, and SQL.
• Develop Retrieval-Augmented Generation (RAG) solutions using enterprise data stored in Snowflake.
• Integrate Large Language Models (LLMs) into enterprise applications using Snowflake Cortex.
• Design and optimize AI workflows for document summarization, sentiment analysis, classification, translation, question answering, and text generation.
• Develop scalable data pipelines to support AI and machine learning workloads.
• Collaborate with Data Engineers, Data Scientists, and business stakeholders to understand AI use cases and deliver effective solutions.

Ensure AI solutions comply with enterprise security, governance, and data privacy standards.

• Optimize Snowflake performance and AI workloads for scalability and cost efficiency.
• Participate in architecture discussions, code reviews, and technical documentation.

Required Skills & Experience

• 5+ years of experience in Data Engineering, AI/ML, or Analytics.
• Strong hands-on experience with Snowflake.
• Experience working with Snowflake Cortex AI capabilities.
• Strong understanding of Snowpark (Python or SQL).
• Experience building AI-powered applications using enterprise data.
• Hands-on experience with Python and SQL.
• Knowledge of Generative AI, Prompt Engineering, and Large Language Models (LLMs).
• Experience designing and implementing RAG (Retrieval-Augmented Generation) solutions.
• Strong understanding of data modeling and data warehousing concepts.
• Experience developing and optimizing ETL/ELT pipelines.
• Experience integrating REST APIs and external AI services. 

Technical Skills

• Snowflake
• Snowflake Cortex AI
• Snowpark
• SnowSQL
• Snowpipe
• Streams & Tasks
• Secure Data Sharing
• Performance Optimization
• Role-Based Access Control (RBAC) 

Programming

• Python
• SQL
• AI & Machine Learning
• Generative AI
• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Model Integration
• Text Analytics
• Semantic Search
• Data Engineering
• ETL/ELT Development
• Data Warehousing
• Data Pipelines
• Structured & Semi-Structured Data Processing
• Cloud (Preferred)
• AWS / Azure / GCP

Preferred Skills

• Experience with vector search and semantic search concepts.
• Knowledge of Snowflake Cortex Analyst, Cortex Search, or Cortex Agents.
• Experience with AI governance and responsible AI practices.
• Familiarity with LangChain, LangGraph, or similar AI orchestration frameworks.
• Exposure to ML model deployment and MLOps practices.
• SnowPro certification is an added advantage. 

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or a related field.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to translate business requirements into AI-driven solutions.
• Strong collaboration and teamwork skills.
• Self-driven with the ability to work independently in a remote environment.

About the Role

We are looking for a skilled SQL Server DBA with 4–5 years of experience in SQL Server database administration, performance tuning, and enterprise data integration. The ideal candidate should have hands-on experience working with Product Lifecycle Management (PLM) systems, ERP integrations, and data bridge solutions to enable seamless data exchange between enterprise applications.

Key Responsibilities

· Administer, monitor, and maintain Microsoft SQL Server databases to ensure high availability, security, and performance.

· Design, implement, and support PLM–ERP data bridge solutions for seamless integration between Product Lifecycle Management and ERP systems.

· Develop and optimize SQL queries, stored procedures, views, triggers, and database objects.

· Monitor database performance and perform query optimization, indexing, and troubleshooting.

· Design and implement database backup, recovery, disaster recovery, and high availability strategies.

· Build and maintain ETL processes and data synchronization workflows between PLM, ERP, and other enterprise applications. 

· Collaborate with application development teams to support database design and application deployments.

· Perform database migrations, upgrades, patching, and environment maintenance.

· Ensure database security, user management, and compliance with organizational standards.

· Create and maintain technical documentation, database architecture, and operational procedures. 

Required Skills & Experience

· 4–5 years of hands-on experience as a SQL Server DBA.

· Strong expertise in Microsoft SQL Server (2016/2019/2022 or later).

· Excellent knowledge of SQL, T-SQL, Stored Procedures, Functions, Triggers, Views, and Performance Tuning.

· Experience in database backup, restore, replication, indexing, and high availability (Always On, Log Shipping, Replication).

· Hands-on experience working with Product Lifecycle Management (PLM) systems.

· Experience implementing or supporting PLM–ERP data bridge/integration solutions.

· Knowledge of ERP systems such as SAP, Oracle E-Business Suite, Microsoft Dynamics, Infor, or similar platforms.

· Experience with ETL tools and enterprise data integration.

· Strong troubleshooting and root cause analysis skills. 

Preferred Skills

· Experience with Teamcenter, Windchill, Enovia, Arena PLM, or similar PLM platforms.

· Knowledge of SSIS, SSRS, and SSAS.

· Experience with PowerShell or Python scripting for database automation.

· Exposure to Azure SQL Database or cloud-based SQL environments. 

· Understanding of manufacturing, engineering, or product development processes.

· Familiarity with CI/CD and DevOps practices. 

Educational Qualification

· Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline.

Key Competencies

· Strong analytical and problem-solving skills.

· Excellent communication and stakeholder management abilities.

· Ability to work independently in a remote environment.

· Strong attention to detail and commitment to database reliability and performance.

· Ability to manage multiple priorities in a fast-paced environment.

Job Description – Data Scientist (Machine Learning & Forecasting)

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, Traditional Statistical Modelling, Forecasting, and Predictive Analytics. The ideal candidate will have hands-on experience building and deploying end-to-end ML solutions, working with large datasets, and translating business problems into scalable data science solutions.

The role requires a strong foundation in statistics, predictive modelling, feature engineering, model evaluation, and time-series forecasting, along with the ability to collaborate with cross-functional teams to deliver business impact.

Key Responsibilities

• Design, develop, and deploy Machine Learning models for business-critical use cases.
• Build and optimize traditional ML models such as:
• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting (XGBoost, LightGBM, CatBoost)
• Support Vector Machines
• Clustering Algorithms
• Develop forecasting solutions using:
• ARIMA / SARIMA
• Prophet
• Exponential Smoothing
• Time-Series Regression Models
• Perform exploratory data analysis (EDA), feature engineering, and data validation.
• Evaluate model performance using appropriate statistical and business metrics.
• Work with structured and semi-structured datasets from multiple sources.
• Collaborate with business stakeholders to understand requirements and translate them into analytical solutions.
• Build scalable data pipelines and support model deployment in production environments.
• Monitor model performance, identify data drift, and implement model retraining strategies.
• Present insights and recommendations to technical and non-technical stakeholders.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field.
• 5+ years of hands-on experience in Data Science, Machine Learning, and Forecasting.

Technical Skills

   Machine Learning

• Strong understanding of supervised and unsupervised learning algorithms.
• Experience with ensemble methods and advanced ML techniques.
• Expertise in model selection, hyperparameter tuning, and performance optimization.

Forecasting & Statistics

• Strong understanding of:
• Time-Series Analysis
• Forecasting Techniques
• Statistical Inference
• Hypothesis Testing
• Probability Distributions
• A/B Testing

Programming

• Advanced proficiency in Python.
• Experience with:
• Pandas
• NumPy
• Scikit-learn
• Statsmodels
• XGBoost / LightGBM
• Prophet

Data & SQL

• Strong SQL skills with experience in complex queries and performance optimization.
• Experience working with large-scale datasets.

Visualization

• Experience with Power BI, Tableau, Matplotlib, Seaborn, or Plotly.
• Cloud & MLOps (Preferred)
• Exposure to AWS, Azure, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and ML model deployment practices.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work independently in a fast-paced environment.
• Strong business acumen and data-driven decision-making mindset. 

Role Overview

We are seeking a hands-on technology POD Lead who blends engineering excellence with statistical rigor and business acumen to drive end-to-end product delivery in an agile, data-driven environment. The ideal candidate will lead a multidisciplinary team of BI developers, data engineers, ML practitioners, and product analysts to accelerate business growth through scalable, AI-enabled products, econometric models and intelligent insights.

This role sits at the intersection of engineering, analytics, econometrics, MLOps and growth strategy, requiring a balance of technical depth, stakeholder engagement, and agile execution.

Key Responsibilities

1. Leadership & Delivery

• Lead a cross-functional pod of data engineers, BI developers, statisticians and machine learning engineers to deliver AI-powered products and analytics solutions.
• Translate strategic goals into data science roadmaps executed in agile sprints, ensuring measurable business outcomes for every release.
• Foster a culture of experimentation, accountability, and rapid iteration across data, AI, and product workstreams.

2. Product & Business Integration

• Partner with business stakeholders across Sales, Marketing, Finance, and Operations to identify high-impact use cases such as churn prediction, growth forecasting, pricing optimization, causal impact analysis or next-best-action recommendations.
• Drive the roadmap for analytical and econometric product capabilities (e.g., predictive dashboards, personalization engines, time-series forecasting, and more).
• Ensure all solutions are aligned with enterprise data strategy, governance, MLOps lifecycle and security standards.

3. Technical Execution

• Collaborate with ML engineers to productionalize models using Databricks, MLflow, Azure ML, or equivalent CI/CD MLOps frameworks.
• Guide teams on feature engineering, model selection, hyperparameter tuning, and validation for statistical and machine learning models.
• Encourage adoption of reusable data assets, API-based integrations, and modular code frameworks.
• Oversee econometric modeling, causal inference studies, and time-series forecasting for business-critical decision-making.
• Champion model lifecycle management, including version control, retraining pipelines, and performance drift monitoring.

4. Business Intelligence & Data Storytelling

• Supervise the creation of advanced BI dashboards and insight layers powered by predictive and generative AI.
• Translate complex statistical outputs into actionable business narratives for executive decision-making.
• Champion KPI alignment and measurement frameworks, ensuring analytics deliver quantifiable value to revenue, growth, retention, and operational efficiency metrics.

5. Agile Program Management

• Manage sprint planning, backlog prioritization, and resource allocation across concurrent projects.
• Track velocity, quality metrics, and ROI impact for each product stream.
• Coach teams on agile best practices and outcome-oriented delivery.

Qualifications

Required

• 10+ years of total experience with at least 3 years in a tech lead capacity.
• Proven expertise in Python, SQL, statistical modeling (e.g., regression, time-series, causal inference) and one or more of Power BI, Tableau, MicroStrategy.
• Strong foundation in data engineering, cloud architecture (Azure/AWS/GCP), and ML model deployment.
• Experience leading cross-functional agile teams with engineers, analysts, and data scientists.
• Excellent communication and stakeholder management skills — capable of simplifying complex data stories for business leaders.

Preferred

• Experience in forecasting models, econometrics, and experimental design (A/B testing, uplift modeling).
• Exposure to MLOps tools (MLflow, Kubeflow, Airflow, Azure ML pipelines) and monitoring frameworks for models in production.
• Familiarity with agentic AI, LLM-based product development, or generative analytics use cases.
• Prior experience building analytics or AI solutions in B2B, SaaS, or digital transformation contexts.
• Certifications in Agile, Cloud (Azure ML, AWS Data Analytics), or Data Science specialization are a plus.

Key Traits

• Hands-on technologist who can code, review, and guide with empathy.
• Strategic thinker who connects product vision with execution.
• Comfortable operating in ambiguity and scaling solutions from POC to enterprise rollout.
• Passionate about mentoring teams and embedding a data-first, growth-oriented mindset.

About the Role

We are looking for a Customer Success Analyst who can work closely with Agile/Scrum teams and bring customer insights into product development. This role will act as a bridge between customers, product, and engineering teams by leveraging data from customer success platforms like Planhat.

Key Responsibilities

• Collaborate with Scrum teams (Product Owner, Developers, Scrum Master) during sprint planning and reviews
• Bring customer insights, feedback, and product usage data into backlog prioritization
• Monitor customer health scores, adoption metrics, and churn risks using tools like Planhat or Gainsight
• Translate customer challenges into actionable user stories and requirements
• Track feature adoption post-release and provide feedback to product teams
• Work closely with Customer Success, Sales, and Support teams to ensure alignment
• Maintain and analyze customer data in CRM tools like Salesforce or HubSpot
• Support renewal and retention strategies by identifying at-risk accounts

Key Requirements

• 5+ years of experience in Customer Success / Business Analysis / Product Support / Account Management
• Basic understanding of Agile / Scrum methodologies
• Experience with Customer Success platforms (Planhat, Gainsight, or similar)
• Strong analytical and problem-solving skills
• Ability to interpret customer data and convert it into actionable insights
• Good communication skills to work with cross-functional teams
• Experience working in SaaS or product-based environments preferred

Good to Have

• Exposure to Agile tools (Jira, Confluence)
• Experience with product analytics tools (Mixpanel, Amplitude)
• Understanding of customer lifecycle management and SaaS metrics (churn, retention, LTV)
• Exposure to accessibility and inclusive product design

Role Overview

We are seeking an experienced Power BI & Microsoft Fabric Consultant to partner with our BI team for a short-term engagement focused on capability building, governance setup, and platform enablement.

The primary objective of this role is to upskill the team and establish a robust, scalable foundation for Power BI and Microsoft Fabric—covering security, governance, administration, and best practices—so that the team can independently manage and scale the platform post-engagement.

This is a hands-on + coaching role, not just advisory.

Key Responsibilities

1. Power BI & Fabric Enablement

• Conduct structured training sessions and workshops on:
• Power BI (Pro + Fabric-integrated experiences)
• Microsoft Fabric (Lakehouse, Warehouse, Dataflows Gen2, Pipelines, etc.)
• Build foundational understanding of:
• End-to-end analytics workflows in Fabric
• Integration between Power BI and Fabric workloads 
• Data Lake will be Snowflake

2. Governance & Security Framework

• Design and implement Power BI & Fabric governance model, including:
• Workspace strategy (Dev/Test/Prod separation)
• Naming conventions and standards
• Content lifecycle management
• Establish security architecture:
• Role-based access control (RBAC)
• Row-Level Security (RLS) / Object-Level Security (OLS)
• Data access patterns across Fabric and Power BI
• Define data sharing and access control processes

3. Administration & Platform Setup

• Configure and optimize:
• Power BI tenant settings
• Fabric capacity (capacity planning, workload management)
• Monitoring and usage metrics
• Set up:
• Deployment pipelines
• CI/CD best practices (where applicable)
• Audit logs and governance controls

4. Best Practices & Standards

• Define and document:
• Development standards (data modeling, DAX, report design)
• Performance optimization guidelines
• Dataset/reusable semantic model strategy
• Establish certification and promotion workflows for datasets and reports

5. Hands-On Implementation

• Work alongside the team to:
• Build or refactor key dashboards/reports using best practices
• Set up Fabric artifacts (Lakehouse/Warehouse/Pipelines)
• Ensure real use cases are implemented, not just theoretical training

6. Knowledge Transfer & Self-Sufficiency

• Provide:
• Playbooks, SOPs, and governance documentation
• Recorded sessions and training materials
• Mentor team members through:
• Office hours / Q&A sessions
• Code and architecture reviews
• Ensure the team can independently:
• Manage Fabric capacity
• Govern Power BI environment
• Implement secure and scalable solutions

Expected Outcomes (End of Engagement)

• Fully defined and implemented Power BI & Fabric governance framework
• Configured and optimized Fabric capacity + Power BI tenant
• Established security and access control processes
• Documented standards, playbooks, and operating model
• BI team capable of independent development, administration, and governance

Required Skills & Experience

Must-Have

• 5+ years of experience in Power BI development and administration
• Hands-on experience with Microsoft Fabric (end-to-end)
• Strong expertise in:
• Power BI governance and tenant administration
• Fabric capacity management
• Security models (RLS, RBAC, data access controls)
• Experience setting up enterprise BI governance frameworks
• Proven track record of training and mentoring teams

Good-to-Have

• Experience with data platforms (Snowflake, Azure, etc.)
• Knowledge of CI/CD for Power BI (DevOps integration)
• Familiarity with data catalog and lineage tools (e.g., Atlan, Alation)
• Understanding of modern data architecture patterns

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, MLOps, and Generative AI. The ideal candidate will have hands-on experience in building scalable ML models, deploying them in production, and working with modern AI frameworks, including GenAI technologies.

Key Responsibilities

·      Design, develop, and deploy machine learning models for real-world business problems

·      Work on end-to-end ML lifecycle: data preprocessing, model building, evaluation, deployment, and monitoring

·      Implement and manage MLOps pipelines for scalable and reproducible workflows

·      Utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management

·      Develop and integrate Generative AI (GenAI) solutions such as LLM-based applications

·      Collaborate with cross-functional teams (engineering, product, business) to translate requirements into AI solutions

·      Optimize model performance and ensure production stability

·      Stay updated with the latest advancements in AI/ML and GenAI ecosystems

Required Skills & Qualifications

·      4+ years of experience in Data Science / Machine Learning

·      Strong programming skills in Python

·      Hands-on experience with ML modeling techniques (supervised, unsupervised, NLP, etc.)

·      Solid understanding of MLOps practices and tools

·      Experience with MLflow or similar model lifecycle tools 

·      Practical experience in Generative AI (GenAI), including working with LLMs

·      Experience with libraries/frameworks like Scikit-learn, TensorFlow, PyTorch

·      Strong understanding of data structures, algorithms, and statistics

·      Experience with cloud platforms (AWS/GCP/Azure) is a plus

Good to Have

·      Experience with LLM fine-tuning, prompt engineering, or RAG pipelines

·      Exposure to Docker, Kubernetes, and CI/CD pipelines

·      Knowledge of data engineering workflows 

Job Description:

1.     Machine Learning Development & Deployment

·      Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross sell/upsell opportunity identification.

·      Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.

·      Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

2.     Cross-Functional ML Use Cases

·      Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.

·      Develop domain-specific models and continuously improve them using feedback loops and real-world performance data. 3.

3.      Model Governance and MLOps

·      Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.

·      Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).

4. Data Engineering and Feature Architecture

·      Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.

·      Perform feature selection, transformation, and engineering aligned with each domain’s business logic. 5. Communication & Stakeholder Collaboration

·      Present technical insights and model results to business and executive stakeholders in a clear, actionable format.

·      Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.

Qualifications:

Required

• Bachelor’s or Master’s degree in (e.g., Computer Science, Engineering, Statistics, Mathematics)

• 4+ years of experience in machine learning, data science.

• Proficiency in Python, XGBoost, PyTorch, TensorFlow, or similar.

• Experience deploying models into production using ML pipelines and orchestration frameworks.

• Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).

• Hands-on experience in implementing machine learning algorithms such as Random Forest, XGBoost, Logistic Regression, and Deep Learning techniques including Neural Networks (ANN, CNN)

Preferred:

• Experience supporting business functions such as Finance, Sales, or Operations with ML use cases.

• Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store).

• Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).

• Background in statistics, forecasting, optimization, or recommendation systems.

Hi ,

We are looking for Oracle Incentive Compensation & Order Management Techno-Functional Consultant

PFB the Job Description:

Job Title: Oracle Incentive Compensation & Order Management Techno-Functional Consultant

Experience: 5+ Years

Location: Remote

Key Responsibilities

Oracle EBS & Incentive Compensation

• Design, configure, and implement Oracle Incentive Compensation (OIC) solutions within Oracle EBS R12.
• Analyze and integrate sales compensation plans, quota management, and commission calculations.
• Configure Plan Elements, Rate Tables, Compensation Plans, Pay Groups, and Sales Rep structures.
• Develop and enhance commission and incentive reporting frameworks.

Technical Development

• Develop PL/SQL packages, procedures, and functions for enterprise applications.
• Build RICEW components (Reports, Interfaces, Conversions, Extensions, Workflows).
• Create XML Publisher (BI Publisher) reports using RTF and XSLT templates.
• Develop and enhance custom workflows and concurrent programs.

Integration & Cloud Technologies

• Design and implement integration solutions using Oracle Integration Cloud (OIC).
• Develop REST web services using Oracle EBS Integrated SOA Gateway (ISG).
• Build integrations between Oracle EBS and external applications (Salesforce, ERP Cloud, third-party systems).
• Implement inbound and outbound interfaces for enterprise data exchange.

Implementation & Support

• Participate in full lifecycle implementations, upgrades, and system stabilization projects.
• Conduct requirements gathering, PRD preparation, and functional/technical design documentation.
• Perform unit testing, integration testing, and UAT support.
• Provide production support and issue resolution for business-critical applications.

Data Migration & Bulk Data Handling

• Use SQL*Loader, Export/Import utilities, and data loaders (FBDI, HCM DL) for large-scale data migration.
• Manage data conversion from legacy systems to Oracle EBS/Fusion applications.

Technical Skills

ERP & Cloud Platforms

• Oracle E-Business Suite R12
• Oracle Incentive Compensation (OIC / ICM)
• Oracle Fusion ERP Cloud (Finance & SCM)
• Oracle Integration Cloud (OIC)

Development Technologies

• PL/SQL
• SQL
• XML / XSLT
• REST Web Services

Tools & Utilities

• TOAD
• PL/SQL Developer
• SQL*Loader
• Oracle Forms 6i
• Oracle Reports 6i
• BI/XML Publisher

Databases

• Oracle 9i / 10g

Functional Knowledge

• CRM Foundation
• Core HR
• P2P, O2C cycles
• Order Management, Inventory, Purchasing