AWS Admin-Terraform Consultant
Responsibilities
AWS Cloud Administration
Manage and maintain AWS infrastructure (EC2, S3, VPC, IAM, RDS, Lambda, etc.)
Monitor system performance, availability, and security
Perform resource provisioning, scaling, and maintenance
Infrastructure as Code (Terraform)
Design and implement Infrastructure as Code (IaC) using Terraform
Develop reusable Terraform modules and templates
Manage Terraform state files and backend configurations
Automate deployment of cloud infrastructure
Automation & DevOps
Build and maintain CI/CD pipelines for infrastructure deployments
Automate operational tasks using scripting (Shell/Python)
Integrate Terraform with tools like Jenkins, GitHub Actions, Azure DevOps
Security & Governance
Implement IAM roles, policies, and security best practices
Enforce compliance standards and governance policies
Configure secure networking (VPC, subnets, security groups, NACLs)
Monitoring & Troubleshooting
Use monitoring tools like CloudWatch, CloudTrail
Troubleshoot infrastructure issues and optimize performance
Maintain logs and audit trails
Cost Optimization
Analyze AWS usage and optimize cost (right-sizing, reserved instances, scaling)
Implement tagging strategies for cost tracking
Required Skills & Qualifications
Core Technical Skills
3–6 years of experience in AWS Administration / Cloud Engineering
Hands-on experience with Terraform (mandatory)
Strong knowledge of AWS services:
EC2, S3, RDS
VPC, IAM
Lambda, API Gateway
Experience with Infrastructure as Code (IaC) principles
DevOps & Tools
Experience with CI/CD pipelines
Proficiency in Git version control
Familiarity with Docker / Kubernetes (preferred)
Scripting & Programming
Proficiency in Shell scripting / Python
Knowledge of automation frameworks
Additional Responsibilities
AWS Certifications (AWS Solutions Architect, SysOps Admin, DevOps Engineer)
Experience with multi-account AWS architecture
Knowledge of configuration management tools (Ansible, Chef, Puppet)
Experience with cloud security and compliance frameworks
Technical and Professional Requirements
- Primary skills: Technology->Cloud Platform->Amazon Webservices DevOps Admin, Terraform
Preferred Skills
- Amazon Webservices DevOps
- Terraform
- Administrative tools
Educational Requirements
MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech
Snowflake
Responsibilities
A day in the life of an Infoscion
- As part of the Infosys consulting team, your primary role would be to actively aid the consulting team in different phases of the project including problem definition, effort estimation, diagnosis, solution generation and design and deployment
- You will explore the alternatives to the recommended solutions based on research that includes literature surveys, information available in public domains, vendor evaluation information, etc. and build POCs
- You will create requirement specifications from the business needs, define the to-be-processes and detailed functional designs based on requirements.
- You will support configuring solution requirements on the products; understand if any issues, diagnose the root-cause of such issues, seek clarifications, and then identify and shortlist solution alternatives
- You will also contribute to unit-level and organizational initiatives with an objective of providing high quality value adding solutions to customers.
If you think you fit right in to help our clients navigate their next in their digital transformation journey, this is the place for you!
Technical and Professional Requirements
- Primary skills:Technology->Data on Cloud-DataStore->Snowflake
Preferred Skills
- Snowflake
Educational Requirements
MCA,MTech,Bachelor of Engineering,BCA,BTech
Python+Spark Scala
Responsibilities
Big Data & Spark Development
Develop and maintain data processing pipelines using Apache Spark (PySpark & Scala)
Work with Spark DataFrames, RDDs, and Spark SQL
Implement transformations, joins, aggregations, and optimizations
Tune Spark jobs for performance, scalability, and reliability
Python & Scala Programming
Write clean, efficient, and scalable code in Python and Scala
Develop modular and reusable components
Integrate data pipelines with various applications and APIs
ETL & Data Engineering
Design and build ETL workflows for structured and unstructured data
Extract data from multiple sources (databases, APIs, flat files)
Perform data cleansing, transformation, and validation
Ensure data accuracy, consistency, and completeness
Data Platforms & Integration
Work with Hadoop ecosystem (HDFS, Hive, Spark)
Handle large datasets in data lakes and warehouses
Process data in formats like Parquet, ORC, JSON, CSV
Collaboration & Support
Work with data engineers, analysts, and business stakeholders
Troubleshoot pipeline issues and provide production support
Participate in Agile/Scrum processes
Maintain technical documentation
Additional Responsibilities
Core Skills
2–5 years of experience in Python development
Hands-on experience with Apache Spark (PySpark and/or Scala)
Strong understanding of data processing and ETL concepts
Good knowledge of SQL and relational databases
Technical and Professional Requirements
- Primary skills:Technology->Big Data – Data Processing->Spark,Technology->Java->Apache,Technology->Machine Learning->Python
Preferred Skills
- PYTHON
- SparkSQL
- Scala
Educational Requirements
MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech
AWS Admin-Terraform-IDQ/Atacamma(Data Quality tool)
Responsibilities
Key Responsibilities
AWS Cloud Administration
Manage and maintain AWS services (EC2, S3, RDS, Lambda, VPC, IAM)
Provision, monitor, and optimize cloud infrastructure
Ensure high availability, scalability, and security of cloud resources
Infrastructure as Code (Terraform)
Design and implement Infrastructure as Code (IaC) using Terraform
Build reusable Terraform modules and templates
Manage remote state, versioning, and automated deployments
Integrate Terraform with CI/CD pipelines
Data Quality Implementation (IDQ / Ataccama)
Install, configure, and manage Informatica Data Quality (IDQ) / Ataccama ONE platform
Implement data profiling, cleansing, validation, and monitoring rules
Ensure data accuracy, completeness, and consistency across systems
Create and maintain data quality workflows and pipelines
Integration & Data Engineering Support
Integrate data quality tools with data lakes/warehouses (Snowflake, Redshift, BigQuery, etc.)
Support ETL/ELT pipelines and validate data at different stages
Work with structured and unstructured datasets
Security & Governance
Implement IAM roles, policies, and access controls in AWS
Ensure data governance and compliance (GDPR, HIPAA, etc. if applicable)
Define and enforce data quality standards and policies
Monitoring & Troubleshooting
Monitor infrastructure using CloudWatch, CloudTrail
Track data quality issues and resolve anomalies
Debug failures in data pipelines and cloud deployments
Automation & DevOps
Automate cloud and data workflows using scripting (Python / Shell)
Build CI/CD pipelines using Jenkins, GitHub Actions, Azure DevOps
Support DevOps best practices in data platforms
Required Skills & Qualifications
Core Skills
3–6 years of experience in AWS Administration / Cloud Engineering
Hands-on experience with Terraform (mandatory)
Experience with data quality tools (Informatica IDQ or Ataccama)
AWS & Cloud
Strong knowledge of:
EC2, S3, RDS
VPC, IAM
Lambda (optional but preferred)
Experience in multi-environment (dev/test/prod) setups
Data Quality Tools
Expertise in:
Informatica Data Quality (IDQ) or
Ataccama ONE / Ataccama DQ
Knowledge of:
Data profiling
Data cleansing & validation
Rule-based quality frameworks
Additional Responsibilities
AWS Certifications (Solutions Architect / SysOps / DevOps Engineer)
Informatica / Ataccama certifications (preferred)
Experience with data governance tools (Collibra, Purview)
Exposure to big data technologies (Spark, Hadoop)
Technical and Professional Requirements
- Primary skills: AWS Admin-Terraform-IDQ/Atacamma(Data Quality tool)
Preferred Skills
- Amazon Webservices DevOps
- Terraform
- ETL & Data Quality – ALL
Educational Requirements
MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech
SAP SD CONSULTANT
Responsibilities
Key Responsibilities
Lead end-to-end SAP SD implementations, rollouts, and enhancements
Gather and analyze business requirements and design scalable SD solutions
Configure core SAP SD modules including:
Sales Order Processing
Pricing & Billing
Credit Management
Shipping and Delivery Processing
Drive Order-to-Cash (OTC) process optimization
Integrate SAP SD with:
MM (Materials Management)
FI (Finance)
TM / EWM (preferred)
Lead client workshops, blueprinting sessions, and solution design discussions
Manage stakeholder communication and provide advisory support
Support cutover planning, data migration, and go-live activities
Troubleshoot complex production issues and provide resolution
Lead or mentor a team of consultants (for Lead role)
Ensure documentation, testing (UAT), and quality assurance
Required Skills & Qualifications
Technical Skills
Strong expertise in SAP SD configuration and customization
Hands-on experience in:
Pricing procedures, condition techniques
Billing processes and outputs
Credit management (FSCM preferred)
Experience with S/4HANA SD (conversion or greenfield implementation)
Knowledge of integration via IDoc, APIs, EDI, and middleware (CPI/PI/PO)
Basic ABAP knowledge (debugging, enhancements, user exits, BADIs)
Experience with SAP Fiori apps for SD
Additional Responsibilities
Preferred Skills (Nice-to-Have)
Experience with S/4HANA and Fiori UI
Exposure to SAP FSCM (Credit Management)
Knowledge of SAP TM / EWM integration
Experience in global rollout and multi-country projects
Familiarity with SAP BTP, CPI integration
Experience with rebates, chargebacks, and contract management
Knowledge of Agile methodologies (Scrum, JIRA)
Exposure to SAP Analytics tools (SAC, Power BI)
Understanding of e-invoicing / compliance frameworks (GST, VAT, etc.)
Technical and Professional Requirements
- Primary skills:Technology->SAP Functional->SAP SD
Preferred Skills
- SAP SD
Educational Requirements
MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech
AWS Bedrock Developer-Terraform-Python Consultant
Responsibilities
AWS Bedrock & Generative AI Development
Design and develop applications using AWS Bedrock (LLMs, Foundation Models)
Integrate models like Anthropic Claude, Amazon Titan, or other Bedrock-supported models
Build prompt engineering strategies and AI workflows
Implement use cases such as chatbots, summarization, recommendations, and NLP pipelines
Python Development
Develop backend services and APIs using Python (FastAPI, Flask)
Implement AI/ML pipelines and integrate model responses into applications
Build reusable libraries for LLM interaction and automation
Infrastructure as Code (Terraform)
Design and implement cloud infrastructure using Terraform
Build reusable modules for deploying AWS resources
Manage Terraform state and automate deployments via CI/CD
Provision services like Bedrock, Lambda, API Gateway, S3, IAM
AWS Cloud Development
Work with key AWS services:
Lambda, API Gateway (serverless apps)
S3 (data storage)
DynamoDB / RDS (data persistence)
IAM & VPC (security & networking)
Build scalable and secure cloud-native applications
DevOps & Automation
Implement CI/CD pipelines using GitHub Actions, Jenkins, or AWS CodePipeline
Automate deployments and testing of AI applications
Monitor applications using CloudWatch, X-Ray
Security & Governance
Implement secure access to Bedrock models using IAM policies
Ensure compliance, data privacy, and secure prompt handling
Manage API authentication and authorization
Required Skills & Qualifications
Core Skills
3–6 years of experience in Python development / Cloud engineering
Hands-on experience with AWS (mandatory)
Experience or exposure to AWS Bedrock / Generative AI services
Programming & Frameworks
Strong proficiency in Python
Experience with:
FastAPI / Flask
REST API development
Familiarity with AI/ML libraries (optional: LangChain, Transformers)
Terraform & DevOps
Hands-on experience with Terraform (IaC)
Experience with:
Infrastructure automation
CI/CD pipelines
Git version control
AWS Services
Core services:
Lambda, API Gateway
S3, DynamoDB/RDS
IAM, VPC
Exposure to serverless architecture
Additional Responsibilities
Experience with LLM frameworks (LangChain, LlamaIndex)
Knowledge of prompt engineering and RAG (Retrieval-Augmented Generation)
AWS Certifications (Solutions Architect / AI Specialty)
Experience with Docker / Kubernetes
Technical and Professional Requirements
- Primary skills:AWS Bedrock Developer, Terraform & Python
Preferred Skills
- closed models (aws bedrock)
- Terraform
- PYTHON
Educational Requirements
MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech
Data Governance (Data Quality tool)
Responsibilities
Data Governance Implementation
Design and implement data governance frameworks, policies, and standards
Define data ownership, stewardship, and accountability models
Ensure compliance with regulatory requirements (GDPR, HIPAA, etc.)
Data Quality Management
Implement and manage data quality tools (IDQ / Ataccama / Collibra / Talend DQ)
Define and enforce data quality rules (validity, accuracy, completeness, consistency)
Perform data profiling, cleansing, validation, and monitoring
Identify and resolve data anomalies and inconsistencies
Metadata & Data Lineage
Maintain and manage data catalog, metadata, and lineage
Track end-to-end data flow across systems
Ensure transparency and traceability of data assets
Data Stewardship & Compliance
Collaborate with data stewards, business users, and IT teams
Establish processes for data issue management and resolution
Conduct audits and ensure adherence to governance policies
Integration with Data Platforms
Integrate data quality checks with:
Data warehouses (Snowflake, BigQuery, Redshift)
Data lakes (AWS S3, Azure Data Lake)
Validate data across ETL/ELT pipelines
Support data engineering and analytics teams
Monitoring & Reporting
Build dashboards and reports on data quality metrics and KPIs
Monitor data health and generate quality scorecards
Provide actionable insights for improvement
Required Skills & Qualifications
Core Skills
3–6 years of experience in Data Governance / Data Quality
Hands-on experience with data quality tools:
Informatica Data Quality (IDQ)
Ataccama ONE
Collibra Data Quality
Talend Data Quality
Strong understanding of data governance principles
Technical Skills
Strong SQL skills
Experience with data profiling and cleansing techniques
Knowledge of ETL/ELT processes and data pipelines
Familiarity with data modeling concepts
Tools & Platforms
Data catalog & governance tools (Collibra, Alation, Azure Purview)
Cloud platforms (AWS / Azure / GCP)
BI tools (Power BI, Tableau) for reporting
Additional Responsibilities
Certifications in Informatica / Ataccama / Collibra / Data Governance
Experience with data lineage and metadata tools
Knowledge of data privacy and security frameworks
Exposure to big data technologies (Spark, Hadoop)
Technical and Professional Requirements
- Primary skills:Technology->Consulting – Data Governance->Data Governance,Technology->ETL & Data Quality->ETL & Data Quality – ALL
Preferred Skills
- Data Governance
- ETL & Data Quality – ALL
Educational Requirements
MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech
Informatica Admin-UNIX Consultant
Responsibilities
Informatica Administration
Install, configure, and upgrade Informatica PowerCenter / IDQ / IICS environments
Configure domains, repositories, integration services, and nodes
Manage users, roles, and security settings within Informatica
UNIX / Linux Administration
Perform server administration on UNIX/Linux environments
Manage file systems, permissions, and system performance
Write and maintain shell scripts for automation and monitoring
Performance Tuning & Optimization
Monitor and optimize Informatica workflows, sessions, and mappings
Tune repository performance and database connectivity
Analyze logs and troubleshoot performance bottlenecks
Deployment & Migration
Handle migration of objects across environments (Dev → QA → Prod)
Use deployment tools and scripts for release management
Maintain version control and environment synchronization
Monitoring & Troubleshooting
Monitor Informatica services using Admin Console, logs, and monitoring tools
Diagnose and resolve failures in workflows, sessions, and services
Perform root cause analysis for recurring issues
Database & Integration Support
Work with databases (Oracle, SQL Server, DB2, etc.)
Optimize connections and troubleshoot database-related issues
Ensure smooth data flow between systems
Security & Governance
Implement security policies, access control, and authentication mechanisms
Ensure compliance with organizational and regulatory standards
Maintain audit logs and access tracking
Backup & Recovery
Perform repository backups, restores, and disaster recovery planning
Ensure data integrity and availability
Maintain high availability (HA) configurations
Required Skills & Qualifications
Core Skills
3–6 years of experience in Informatica Administration
Strong experience with:
Informatica PowerCenter
Informatica IDQ (preferred)
Strong UNIX/Linux administration skills (mandatory)
Additional Responsibilities
xperience with:
Informatica Admin Console
Monitoring tools
Version control (Git preferred)
Preferred Qualifications
Informatica certification (PowerCenter Admin / IDQ)
Experience with Informatica Cloud (IICS)
Knowledge of ETL/ELT processes
Familiarity with cloud platforms (AWS / Azure / GCP)
Technical and Professional Requirements
- Primary skills:Technology->Data Management – Data Integration Administration->Informatica Administration & Unix
Preferred Skills
- Informatica Administration
Educational Requirements
MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech
Scala, Spark/pyspark
Responsibilities
Big Data & Spark Development
Design and implement scalable data pipelines using Apache Spark (Scala and/or PySpark)
Work extensively with Spark Core, Spark SQL, DataFrames, and Datasets
Develop batch and real-time data processing solutions using Spark Streaming / Structured Streaming
Optimize Spark jobs for performance, memory management, and parallel processing
Scala & Python Development
Develop robust and efficient applications using Scala and Python
Write reusable, modular, and maintainable code
Implement business logic and transformations on large datasets
Data Engineering & ETL
Build and maintain ETL/ELT pipelines for large-scale data ingestion and transformation
Process structured and unstructured data from multiple sources
Ensure data validation, quality, and consistency
Work with file formats like Parquet, ORC, Avro, JSON, CSV
Big Data Ecosystem
Work with Hadoop ecosystem (HDFS, Hive, YARN)
Integrate Spark jobs with data lakes and warehouses
Handle large datasets with distributed computing techniques
Cloud & Integration (Optional but Preferred)
Work with cloud platforms (AWS/Azure/GCP) for big data solutions
Utilize services such as AWS EMR, Glue, S3 / Azure Databricks / Synapse
Integrate pipelines with APIs and external systems
Collaboration & Leadership
Collaborate with data engineers, architects, and business teams
Lead technical discussions and provide guidance to junior developers
Participate in code reviews and best practice implementation
Work in Agile/Scrum environments
Additional Responsibilities
Core Skills
5–9 years of experience in data engineering / big data development
Strong hands-on expertise in Scala (mandatory for this role)
Extensive experience with Apache Spark (Scala and/or PySpark)
Solid understanding of ETL processes and data pipelines
Strong proficiency in SQL and database concepts
Technical Skills
Deep knowledge of Spark architecture and execution model
Experience with Spark performance tuning and optimization
Strong data modeling and warehousing concepts
Familiarity with version control tools (Git)
Understanding of distributed computing principles
Preferred Skills
Experience with Spark Streaming / Kafka
Hands-on with Databricks platform
Knowledge of Airflow or workflow orchestration tools
Familiarity with Docker/Kubernetes
Exposure to NoSQL databases (Cassandra, MongoDB, HBase)
Technical and Professional Requirements
- Primary skills:Domain->Finacle-Core-Functional->Finacle-Core-WMS->Grand Master,Technology->Big Data – Data Processing->Spark,Technology->Java->Apache
Preferred Skills
- SparkSQL
- Scala
- PySpark
Educational Requirements
MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech
MLE/MLOps, OOPs Python, Databricks, Azure
Responsibilities
Machine Learning Engineering
Design, develop, and deploy scalable ML models and AI solutions
Build end-to-end pipelines covering data ingestion, feature engineering, model training, evaluation, and deployment
Apply advanced techniques for model optimization, validation, and explainability
Ensure models are production-ready with high accuracy and performance
MLOps & Lifecycle Management
Design and implement MLOps frameworks for CI/CD/CT (continuous training)
Automate model deployment, versioning, monitoring, and rollback strategies
Implement model performance tracking, drift detection, and alerting systems
Use tools like MLflow for experiment tracking and model registry
Python (OOPs) Development
Write scalable, modular, and reusable code using object-oriented Python
Develop APIs and backend services for model serving and integration
Implement best practices for code quality, testing, and maintainability
Databricks & Big Data
Build and optimize pipelines using Azure Databricks and PySpark
Work with Delta Lake for data versioning and reliability
Manage Databricks clusters, jobs, and workflows
Optimize Spark jobs for performance, scalability, and cost efficiency
Azure Cloud Platform
Design ML solutions using Azure services (Azure ML, ADLS, Data Factory, Key Vault, Synapse)
Implement secure and scalable cloud architectures
Integrate ML pipelines with Azure DevOps CI/CD pipelines
Ensure compliance with data governance and security policies
Data Engineering & Integration
Develop robust data pipelines for ML workflows
Handle large-scale structured and unstructured datasets
Integrate ML models with downstream applications via APIs/microservices
Additional Responsibilities
Preferred Skills
Experience with feature stores and model monitoring tools
Knowledge of Docker & Kubernetes (containerization)
Familiarity with streaming (Kafka, Event Hub)
Experience with Lakehouse architecture (Delta Lake)
Exposure to GenAI / LLMOps (optional, added advantage)
Technical and Professional Requirements
- Primary skills:Technology->Data Science->Machine Learning,Technology->Machine Learning->Python
Preferred Skills
- Machine Learning
- PYTHON
Educational Requirements
MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech