PART 1.

Tasks.

Design & Develop: Build robust, scalable, and efficient data pipelines and backend systems using state-of-the-art technologies.
Integrate Systems: Seamlessly integrate data solutions into enterprise ecosystems, ensuring smooth workflows and data consistency.
Collaborate & Innovate: Work closely with cross-functional teams to design, test, and deploy end-to-end solutions that drive measurable outcomes.
Optimize Processes: Analyze and optimize existing workflows for performance, scalability, and reliability.
Model & Organize Data: Develop and maintain comprehensive data models to support robust and scalable applications.
Ensure Data Quality: Implement checks and frameworks to maintain high data quality and system reliability.

Skills overview .

Technical Expertise: Strong proficiency in data engineering and backend technologies, with significant experience in building scalable data systems.
Experience in development of Celonis Platform, Pega Platform and/or Appian Platform
Full Stack Skills: Familiarity with modern web development frameworks and tools (Node.js, React, Angular, etc.) is a plus.
Problem Solver: Exceptional analytical skills with the ability to tackle challenging problems with innovative solutions.
Team Player: Excellent communication and collaboration skills to work effectively in a fast-paced environment.
Passion for Data: A genuine enthusiasm for harnessing the power of data to drive business value.

Technical Skills & Tools

VisFlow (https://www.visflow.io/), is a cutting-edge platform for real-time visualization and analytics.
Programming Languages: Python, SQL
Data Integration & Transformation: DBT, RDF, Knowledge Graphs, Apache Jena
Data Streaming: Apache Kafka, Apache Flink
Business Rules Engine: Knowledge of Business Rule Engines such as Drools, GoRules or other Open Source engines.
Custom Connectors: Airbyte, Apache Nifi
Cloud Technologies: AWS (S3, Lambda, EC2, Glue, etc.)
Orchestration Tools: Apache Airflow
Data Storage & Formats: PostgreSQL, Apache Iceberg, Apache Hudi, Parquet, Avro
Distributed Data Processing: Apache Spark (PySpark), Flink, Dask
Version Control & CI/CD: Git, GitLab CI/CD, Jenkins, GitHub Actions
Containerization & DevOps: Docker, Infrastructure as Code (IaC) with Terraform
Data Governance & Lineage: DataHub, OpenLineage
Data Privacy & Security: Open Policy Agent (OPA), Apache Ranger
Data Query Engines: Trino (formerly Presto), Apache Hive, DuckDB
Data Quality & Validation: Great Expectations

Qualifications

Proven experience in data engineering or full stack development.
Proficiency in programming languages such as Python, Java, or similar.
Experience with AWS cloud platforms and containerization (Docker, Kubernetes) is highly desirable.
Strong knowledge of SQL and database optimization techniques.

Platorm Popularity Rank for This task.

For the task provided, which focuses on data engineering, backend development, and process optimization, here’s a short popularity ranking of Celonis, Pega, and Appian based on relevance and demand:

Popularity Rank for This Job

Celonis
- Why?
  - Best for process optimization, data analysis, and workflow automation.
  - Strong in data-driven insights and process mining, aligning with tasks like optimizing workflows and ensuring data quality.
- Relevance: High for data engineers focused on process improvement and analytics.
Appian
- Why?
  - Excels in low-code automation and rapid application development.
  - Great for integrating systems and building scalable workflows.
- Relevance: Moderate for backend developers and system integrators.
Pega
- Why?
  - Strong in BPM and CRM, but less focused on pure data engineering or backend development.
  - Better suited for customer-centric workflows than technical data pipelines.
- Relevance: Lower for this specific job, unless CRM or case management is a focus.

Summary

#1 Celonis: Best for data-driven process optimization and analytics.
#2 Appian: Great for low-code automation and system integration.
#3 Pega: Least relevant unless CRM or BPM is a key requirement.

PART 2.

Here’s a list of 40+ tools with a short description for each:

Programming Languages

Python: Versatile language for data engineering and scripting.
SQL: Standard language for querying and managing relational databases.
Java: Object-oriented language for backend and enterprise applications.
Node.js: JavaScript runtime for building scalable web applications.
React: JavaScript library for building user interfaces.
Angular: Framework for building dynamic web applications.

Data Integration & Transformation

DBT (Data Build Tool): SQL-based tool for data transformation.
RDF (Resource Description Framework): Framework for representing linked data.
Knowledge Graphs: Graph-based data models for semantic relationships.
Apache Jena: Java framework for building semantic web applications.
Airbyte: Open-source data integration platform.
Apache Nifi: Tool for data flow automation and integration.

Data Streaming

Apache Kafka: Distributed streaming platform for real-time data.
Apache Flink: Stream processing framework for real-time analytics.

Business Rules Engines

Drools: Open-source business rules management system.
GoRules: Business rules engine for decision management.

Cloud Technologies

AWS S3: Scalable cloud storage service.
AWS Lambda: Serverless compute service for running code.
AWS EC2: Scalable cloud computing service.
AWS Glue: ETL service for data preparation and integration.

Orchestration Tools

Apache Airflow: Platform for programmatically scheduling workflows.

Data Storage & Formats

PostgreSQL: Open-source relational database management system.
Apache Iceberg: Table format for large-scale data lakes.
Apache Hudi: Data management framework for incremental processing.
Parquet: Columnar storage format for efficient data processing.
Avro: Data serialization system for compact binary format.

Distributed Data Processing

Apache Spark: Unified analytics engine for large-scale data processing.
PySpark: Python API for Apache Spark.
Flink: Stream processing framework for real-time analytics.
Dask: Parallel computing library for scalable analytics.

Version Control & CI/CD

Git: Distributed version control system.
GitLab CI/CD: Continuous integration and deployment platform.
Jenkins: Open-source automation server for CI/CD.
GitHub Actions: CI/CD tool integrated with GitHub.

Containerization & DevOps

Docker: Platform for containerizing applications.
Terraform: Infrastructure as Code (IaC) tool for cloud provisioning.
Kubernetes: Container orchestration platform for scaling applications.

Data Governance & Lineage

DataHub: Metadata management platform for data discovery.
OpenLineage: Framework for tracking data lineage.

Data Privacy & Security

Open Policy Agent (OPA): Policy-based control for cloud-native environments.
Apache Ranger: Security framework for Hadoop ecosystems.

Data Query Engines

Trino (formerly Presto): Distributed SQL query engine.
Apache Hive: Data warehouse software for querying large datasets.
DuckDB: In-process SQL OLAP database.

Data Quality & Validation

Great Expectations: Python library for data validation and testing.

Other Tools

VisFlow: Platform for real-time visualization and analytics.
Celonis: Process mining and execution management platform.
Pega: Business process management and CRM platform.
Appian: Low-code automation and process management platform.

Visual Data Flow 1.

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