Role Overview

- Contribute to the development and maintenance of automated data pipelines for ingesting, transforming, and delivering analytics datasets.

- Work with senior engineers to implement data transformations and models that support reporting and analysis.

- Collaborate with data analysts, data scientists, and product teams to understand data requirements and support data flow across systems.

- Assist in data modeling and schema implementation to ensure analytics-friendly data structures.

- Support data quality checks, validation, and monitoring, helping identify inconsistencies and resolve issues.

- Participate in supporting ad-hoc data analysis and queries to help answer business questions.

- Learn and adopt best practices in data engineering, including version control, testing, and CI/CD workflows.

- Contribute to improving data reliability and documentation under guidance from senior team members.

- Leverage AI-assisted tooling and explore building lightweight AI agents or LLM-powered workflows to automate data tasks, accelerate analysis, and improve team productivity.

Desired Skills

- 3+ years of experience building, implementing, and maintaining data warehousing and analytics solutions.

- Hands-on experience with distributed data processing frameworks such as Spark, Hive,



or Iceberg, with a focus on analytics use cases.

- Proficiency in SQL, including writing complex queries and supporting performance optimization for analytical workloads.

- Working knowledge of Python, Java, or Scala for data transformation and pipeline development.

- Experience building and maintaining data pipelines using Spark (SparkSQL) and orchestration tools such as Airflow or equivalent.

- Exposure to MPP analytical databases (e.g., Snowflake, Redshift) and understanding of query performance considerations.

- Exposure to building or extending AI agents and LLM-powered applications. e.g., tool-use/function calling, MCP, or agentic workflows over data along with solid software engineering fundamentals (APIs, services, testing) is a plus.

- Understanding of data modeling concepts and analytics-friendly schema design.

- Familiarity with data warehousing concepts, including schema design, partitioning, and data lifecycle.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Engineering MTS professional (Bengaluru)
🏢 salesforce
📍 Bengaluru