Data & AI Engineer

About the Job

Company: Trida Labs
Location: Pune, India
Work Model: Remote
Employment Type: Full-time
Experience: 3–5 Years

About Trida Labs

Trida Labs builds AI-native products that help organizations work with enterprise data, analytics, and intelligent applications.

Our products include:

• TridaPad — An AI-native data collaboration platform for querying, analyzing, visualizing, and managing data across SQL, NoSQL, Big Data, and cloud data sources.

• AgentSQL — An agentic AI analytics solution that enables users to interact with enterprise data using natural language, generate SQL, analyze data, create visualizations and dashboards, and derive insights.

• TridaPad MCP Server — A Model Context Protocol server that enables AI assistants and MCP-compatible clients to interact with TridaPad and its data capabilities.

About the Role

We are looking for a Data & AI Engineer to build the data connectivity, metadata, and semantic foundations that power TridaPad and AgentSQL.

You will work with enterprise databases and data platforms where customer data remains in its source system. Your work will focus on building reliable connectors, discovering and maintaining schema metadata, handling differences across database engines, and providing accurate context for AI-powered data workflows.

You will help make enterprise data understandable and usable by AI systems while ensuring that queries, metadata, and data access behave correctly across different customer environments.

This role sits at the intersection of data engineering, databases, AI, and enterprise data systems.

What You'll DoEnterprise Data Connectivity

• Build and maintain integrations with relational, NoSQL, cloud, and analytical data platforms.

• Work with databases and platforms such as PostgreSQL, MySQL, SQL Server, MongoDB, Snowflake, BigQuery, and Databricks.

• Develop reliable mechanisms for connecting to and querying customer data sources.

• Handle connection management, timeouts, retries, connection pooling, and other reliability concerns.

• Troubleshoot connectivity and query execution issues across different customer environments.

• Build reusable patterns for supporting new enterprise data sources.

Schema & Metadata Engineering

• Build systems that discover and maintain database schemas and metadata.

• Collect information about tables, columns, relationships, data types, constraints, and other relevant database characteristics.

• Support metadata refresh and synchronization as customer schemas change.

• Detect schema changes that may affect AI-generated queries and data workflows.

• Develop metadata retrieval and representation mechanisms that provide useful context to downstream AI systems.

• Improve the accuracy and freshness of metadata available to AgentSQL.

Semantic Layer & Business Context

• Build and maintain representations of business concepts and their relationships to enterprise data structures.

• Map business terminology, metrics, and concepts to tables, columns, relationships, and database fields.

• Help identify relevant tables, columns, joins, filters, and metrics for user questions.

• Develop mechanisms for representing enterprise data in a way that is useful for AI-powered analytics.

• Improve the quality of context provided to AI systems for data-related tasks.

• Work with product and AI engineers to understand how customers use business terminology with their data.

SQL & Cross-Database Querying

• Develop and optimize SQL queries for different database engines and analytical workloads.

• Handle differences in SQL dialects, syntax, functions, data types, quoting, date operations, and database-specific behavior.

• Ensure generated or constructed queries are compatible with the target database engine.

• Work with complex joins, aggregations, filtering, subqueries, and window functions.

• Understand database-specific execution behavior and query limitations.

• Work with MongoDB aggregation pipelines and other non-SQL query patterns where applicable.

• Investigate query failures and differences in query behavior across supported data sources.

AI Data & Context Workflows

• Build data and metadata workflows that enable AI systems to understand enterprise data.

• Support natural-language-to-SQL and AI-powered analytics workflows.

• Provide relevant schema, metadata, and business context to AI applications.

• Improve table, column, relationship, and metric selection for user queries.

• Support retrieval of relevant enterprise data context for AI workflows.

• Evaluate whether the context provided to AI systems is complete, relevant, and accurate.

• Work with AI engineers to improve the reliability of data-related AI workflows.

Data Access, Privacy & Controls

• Ensure metadata and data-access workflows respect customer permissions and available access rights.

• Support permission-aware discovery of databases, schemas, tables, and other data resources.

• Apply appropriate controls when working with customer data, metadata, and sampled values.

• Support safe handling, filtering, masking, or redaction of sensitive data where required by the product.

• Understand what customer information is required for AI workflows and what information should not be retained or exposed.

• Work with engineering teams to identify and address data-access and privacy risks.

Data Quality & Output Correctness

• Validate that metadata and schema information accurately represents the underlying data source.

• Investigate incorrect table or column mappings, stale metadata, broken relationships, and schema inconsistencies.

• Validate that queries generated or executed through data workflows return results consistent with the requested analysis.

• Investigate issues where SQL, metadata, or retrieved context leads to incorrect or misleading analytical results.

• Build checks and tests that improve the correctness and reliability of data workflows.

• Distinguish issues caused by source data from issues caused by TridaPad's data access and processing layers.

APIs & AI Integrations

• Develop APIs and backend services that expose reliable data and metadata capabilities.

• Integrate enterprise data capabilities with AI applications and services.

• Support MCP-based integrations between AI systems and TridaPad data capabilities.

• Implement appropriate authentication, authorization, and data-access controls.

• Troubleshoot issues across connector, API, database, and application layers.

Performance & Production Engineering

• Identify and resolve performance bottlenecks across connectors, databases, metadata services, and data workflows.

• Optimize queries, metadata retrieval, connection handling, and data-access operations.

• Monitor connector health, database connections, query execution, and critical data services.

• Investigate production issues and perform root-cause analysis.

• Improve the reliability and scalability of enterprise data integrations.

• Support production deployments, releases, and operational improvements.

Engineering & Product Collaboration

• Work closely with AI Agent, NLP, backend, database, platform, and product teams.

• Contribute to technical designs for connectors, metadata systems, and enterprise data workflows.

• Define clear interfaces between the data layer, AI systems, and application services.

• Build reusable integration and metadata components.

• Document supported data sources, schemas, integrations, and technical workflows.

• Identify opportunities to improve enterprise data accessibility and AI reliability.

Role Ownership

• Data & AI Engineering owns enterprise data connectivity, schema and metadata, semantic representations, and cross-database data correctness.

• AI Agent Engineering owns agent orchestration, tool usage, agent workflows, and agent behavior built on these data capabilities.

• ML/NLP Engineering owns model and generation quality for natural-language understanding and SQL generation.

• Database Engineering focuses on database-specific integration, connector implementation, and deeper query/database performance.

What We're Looking For

• 3–5 years of experience in data engineering, software engineering, database engineering, AI engineering, or a related field.

• Strong proficiency in Python and SQL.

• Strong understanding of relational databases, schemas, data modeling, and query execution.

• Hands-on experience integrating or working with multiple database systems.

• Experience building data-access, metadata, or integration services.

• Understanding of SQL dialect differences across database engines.

• Experience troubleshooting database connectivity, queries, and production data issues.

• Understanding of APIs and backend service development.

• Familiarity with AI/LLM applications and Generative AI.

• Understanding of schema-aware AI workflows, RAG, embeddings, or retrieval systems.

• Strong analytical, debugging, and problem-solving skills.

• Strong communication and collaboration skills.

Preferred Qualifications

• Experience with PostgreSQL, MySQL, SQL Server, MongoDB, Snowflake, BigQuery, or Databricks.

• Experience building database connectors or data-source integrations.

• Experience with schema discovery, metadata systems, or semantic/data catalog solutions.

• Experience with SQL dialect translation or cross-database query systems.

• Experience with Pandas, NumPy, or similar data-processing libraries.

• Experience with RAG, embeddings, vector databases, or retrieval pipelines.

• Experience with LangChain, LangGraph, LlamaIndex, or similar AI frameworks.

• Experience with MCP and AI-to-data integrations.

• Experience with natural-language-to-SQL or AI-powered analytics systems.

• Experience with data-access controls, sensitive-data handling, or enterprise data security.

• Experience with Docker and cloud and deployment platforms such as AWS, Azure, GCP, Render, or Vercel.

• Familiarity with CI/CD, testing, observability, and production engineering practices.

Key Skills

Python | SQL | Data Engineering | Database Integration | Data Connectivity | Schema & Metadata | Data Modeling | Semantic Layer | SQL Dialects | PostgreSQL | MySQL | SQL Server | MongoDB | Snowflake | BigQuery | RAG | LLMs | Generative AI | Natural-Language-to-SQL | APIs | MCP | Data Analytics | Docker | Cloud

What We Offer

• Opportunity to work on production AI and enterprise data products.

• Hands-on experience with multiple database systems and enterprise data environments.

• Opportunity to work at the intersection of data engineering and Generative AI.

• Exposure to schema intelligence, natural-language-to-SQL, AI analytics, and enterprise data integrations.

• Opportunity to contribute to the architecture and development of AI-powered data products.

• Collaborative environment focused on solving practical enterprise data and AI problems.

Why Join Trida Labs?

At Trida Labs, you will work on the intersection of enterprise data and AI, helping make complex customer data accessible and understandable to intelligent applications.

You will work across the data connectivity and metadata lifecycle — from connecting to customer data sources and understanding their schemas to providing reliable context for AI-powered analytics.

Equal Opportunity

Trida Labs is an equal opportunity employer. We are committed to creating an inclusive workplace where diverse perspectives are valued and everyone has the opportunity to contribute and grow.