Data Engineer Data Fabric

About Salt Technologies

Salt Technologies is a global software development company specializing in custom software engineering, AI & ML solutions, data engineering, cybersecurity, and digital product development. We partner with startups, SMBs, and enterprises to build scalable, secure, and impactful digital products.

Our culture is built around:

- Ownership and accountability
- Continuous learning
- Customer obsession
- Operational excellence
- Collaboration and transparency

About the Role

As a Data Engineer, Data Fabric, you will design, develop, automate, and optimize data pipelines, data models, and data flows for global clients.

You will work with AWS, Data Fabric architecture, Active Metadata Catalogs, structured/unstructured data, PostgreSQL, MongoDB, and Neo4j. You will build solutions for data profiling, crawling, quality checks, transformation, and secure ingestion into data stores and, where required, Small Language Models (SLMs).

You will work on real-world use cases across supply chains, critical minerals, and climate change, collaborating with Data Engineering, AI/ML, Cloud/DevOps, and Full Stack teams.

Core Responsibilities

- Design, develop, and optimize scalable data pipelines and ETL/ELT workflows.
- Build automated data ingestion, transformation, validation, and loading processes.
- Develop Common Data Models and metadata-driven data solutions.
- Apply Data Fabric principles and work with Active Metadata Catalogs.
- Perform data profiling, crawling, quality checks, and data transformation.
- Build pipelines using AWS Glue, Data Catalog, Lake Formation, and Data Lake.
- Develop stream-processing solutions using Kafka.
- Work with PostgreSQL, MongoDB, and Neo4j.
- Develop data automation scripts using Python and other scripting languages.
- Support data integration with SLMs and AI applications.
- Monitor and optimize pipeline performance, reliability, and scalability.

Delivery Responsibilities

- Deliver production-ready data solutions within agreed timelines.
- Participate in requirements, design, development, testing, deployment, and support.
- Troubleshoot pipeline, data quality, integration, and performance issues.
- Build reusable and scalable data engineering solutions.




- Maintain proper documentation and development standards.

Data Architecture & Modeling Responsibilities

- Develop and maintain Common Data Models.
- Design data flows across multiple data sources and data stores.
- Work with structured, semi-structured, and unstructured data.
- Design solutions using relational, NoSQL, and graph databases.
- Support data catalogs, metadata, lineage, and data products.

AWS & Data Pipeline Responsibilities

- Build and maintain AWS-based data pipelines.
- Work with AWS Glue, Data Catalog, Lake Formation, and Data Lake.
- Optimize AWS data processing for performance and cost.
- Support secure data movement and processing across AWS environments.

Data Quality & Governance Responsibilities

- Implement data profiling, validation, cleansing, and quality checks.
- Maintain data metadata, lineage, and documentation.
- Ensure secure handling of customer and enterprise data.
- Follow data privacy, security, access control, and governance requirements.

Collaboration Responsibilities

- Collaborate with Data, AI/ML, Full Stack, and Cloud/DevOps teams.
- Work with AI/ML teams to support LLM, SLM, RAG, and AI applications.
- Participate in architecture discussions, code reviews, and technical planning.
- Translate business requirements into scalable data solutions.

Process & Quality Responsibilities

- Follow data engineering and software development best practices.
- Maintain clean, reusable, and documented code.
- Implement testing and data validation practices.
- Use version control and CI/CD practices.
- Monitor pipeline performance and continuously improve data solutions.
- Key Outcomes Expected
- Reliable and scalable data pipelines delivered on time.
- Effective implementation of Data Fabric and Active Metadata Catalog principles.
- High-quality Common Data Models and data products.
- Secure and automated data ingestion and processing.
- Effective use of AWS, PostgreSQL, MongoDB, Neo4j,



and Kafka.
- Improved pipeline performance, automation, reliability, and data quality.

Must-Have Qualifications

- 3 to 5 years of professional Data Engineering experience.
- Hands-on experience building data pipelines and ETL/ELT workflows.
- Experience with AWS data engineering technologies.
- Experience with structured and unstructured data.
- Experience with data ingestion, transformation, profiling, and quality checks.
- Experience with relational, NoSQL, and graph databases.

Technical Skills

- Strong knowledge of Data Engineering, Data Architecture, and Data Modeling.
- Hands-on experience with AWS Glue, Data Catalog, Lake Formation, and Data Lake.
- Experience with Kafka / stream processing.
- Robust Python and SQL skills.
- Experience with PostgreSQL, MongoDB, and Neo4j.
- Understanding of Common Data Models, Data Fabric, metadata, and data lineage.
- Knowledge of data security and governance practices.

Data Engineering Skills

- Data Pipelines
- ETL / ELT
- Data Modeling
- Data Fabric
- Active Metadata Catalog
- Data Ingestion
- Data Profiling
- Data Quality
- Data Transformation
- Stream Processing
- Data Automation

Tools & Platforms

- AWS Glue
- AWS Data Catalog
- AWS Lake Formation
- AWS Data Lake
- Kafka
- PostgreSQL
- MongoDB
- Neo4j
- Python
- Apache Spark
- Informatica
- Git / GitHub

Nice-to-Have Qualifications

- Experience with AWS Cloud infrastructure and resource management.
- Experience with Spark, Informatica, or Oracle Fusion.
- Experience with Agentic AI frameworks and AI-driven data pipelines.
- Interest or experience in AI/ML, Data Science, or Software Engineering.
- Experience integrating data pipelines with LLMs, SLMs, or RAG applications.
- Experience with Data Fabric and metadata-driven architectures.
- Exposure to supply chain, critical minerals, climate change, or sustainability datasets.

Behavioral Competencies

- We're looking for someone who demonstrates:
- Ownership and accountability
- Analytical and problem-solving skills
- Collaboration and teamwork
- Strong communication
- Attention to detail
- Adaptability and continuous learning
- Customer focus
- Ability to work independently
- Ability to manage multiple priorities and deadlines

📌 Data Engineer - Data Fabric (Pune)
🏢 Salt web technologies
📍 Pune