Job Overview

Salesforce is hiring for a Data Engineering MTS position in Bangalore. This opportunity is suited to experienced data engineers who want to build and maintain the data infrastructure that supports analytics, reporting, product, marketing, sales, and operations functions across a large enterprise technology organization.

The role focuses on developing reliable data pipelines, transforming information into analytics-ready datasets, supporting data quality, and helping teams make better decisions through trusted data. You will work alongside senior engineers, analysts, data scientists, product teams, and business stakeholders to understand data requirements and turn them into practical engineering solutions.

The position also provides exposure to modern data engineering practices and emerging AI-assisted workflows. Candidates interested in data engineering jobs, SQL jobs, cloud jobs, Big Data jobs, and Salesforce software jobs in Bangalore may find this role particularly relevant.

Key Responsibilities

• Contribute to automated pipelines that ingest, transform, validate, and deliver data for analytics use cases.
• Develop data transformations and models under the guidance of senior engineers.
• Work with analysts, data scientists, product teams, and business stakeholders to understand requirements and improve data flows.
• Help implement analytics-friendly schemas and data models.
• Support data validation, quality checks, monitoring, and issue resolution.
• Investigate data inconsistencies and help improve reliability across data systems.
• Perform ad-hoc analysis and SQL queries to help answer business questions.
• Follow engineering practices such as version control, testing, documentation, and CI/CD.
• Contribute to improvements in pipeline reliability and maintainability.
• Work with distributed data processing technologies for large-scale analytics workloads.
• Support data integration from databases and external systems through APIs and standard ingestion methods.
• Explore AI-assisted development tools and contribute to lightweight AI agents or LLM-powered workflows that can automate data-related activities.

Required Skills

Candidates should have at least 3 years of experience building, implementing, and maintaining data warehousing or analytics solutions. Strong SQL capability is important, including the ability to write complex analytical queries and understand performance considerations.

Hands-on experience with distributed data processing frameworks such as Apache Spark, Hive, or Iceberg is required. Candidates should understand how distributed processing can be applied to analytics workloads and large datasets.

Programming experience in Python, Java, or Scala is expected for data transformation and pipeline development. Experience with SparkSQL is particularly relevant, as is familiarity with orchestration platforms such as Apache Airflow or equivalent tools.

The role also requires a practical understanding of data modeling, schema design, partitioning, data warehousing concepts, and data lifecycle management. Experience integrating information from multiple sources, including databases and third-party systems, is valuable.

Preferred Skills

Experience with MPP analytical databases such as Snowflake or Amazon Redshift is useful, especially for professionals who understand analytical query performance and large-scale data workloads.

Experience with cloud platforms, preferably AWS, is also desirable. Candidates who have worked with cloud-based data infrastructure and data ingestion services can bring relevant practical knowledge.

Exposure to AI agents and LLM-powered applications is an additional advantage. Familiarity with tool use, function calling, MCP, or agentic workflows over data can help candidates contribute to emerging automation initiatives.

Experience using AI-assisted developer tools such as Cursor or Claude Code is a plus. These tools are relevant to the role because the team is exploring ways to improve engineering productivity through AI-supported development and data workflows.

Education

The supplied job description does not specify a mandatory academic qualification. Candidates should demonstrate relevant professional experience in data engineering and analytics engineering, supported by strong technical skills in SQL, programming, distributed processing, data modeling, and cloud technologies.

Experience

A minimum of 3 years of experience building, implementing, and maintaining data warehousing and analytics solutions is stated. Successful candidates should be comfortable contributing to production data pipelines and working with senior engineers on complex data infrastructure.

Relevant experience may include building Spark-based pipelines, writing advanced SQL, designing schemas, orchestrating workflows with Airflow, integrating multiple data sources, supporting data quality, and working with cloud data platforms.

Required Technologies

• SQL
• Python
• Java
• Scala
• Apache Spark
• SparkSQL
• Hive
• Iceberg
• Apache Airflow
• Data Warehousing
• Data Modeling
• Data Pipelines
• AWS

Additional Technologies and Tools

• Snowflake
• Amazon Redshift
• APIs
• CI/CD
• Version Control
• AI Agents
• LLMs
• MCP
• Cursor
• Claude Code

Soft Skills

Communication and collaboration are important because the role requires regular interaction with engineers, analysts, data scientists, product teams, and business stakeholders. Candidates should be able to explain data requirements and technical findings clearly to both technical and non-technical colleagues.

A willingness to learn is also important. The role combines established data engineering practices with emerging AI-assisted workflows, so adaptability and curiosity can help engineers contribute effectively as tools and approaches evolve.

A practical and ownership-oriented approach is valuable as well. Data engineers should be prepared to investigate data quality issues, improve reliability, document solutions, and follow problems through to resolution.

Benefits of Working in this Role

The supplied job description lists several benefits and development opportunities associated with the role. These include a comprehensive benefits package, well-being reimbursement, parental leave, adoption assistance, fertility benefits, world-class enablement and on-demand training through Trailhead, exposure to executive thought leaders, regular one-to-one coaching with leadership, and opportunities to participate in community volunteering.

The role also offers the opportunity to work on data infrastructure that supports multiple business functions. This can provide broad exposure to enterprise data engineering, analytics, cloud technologies, distributed processing, and AI-assisted development.

Work Mode

Salesforce embraces a hybrid working model. The position is based in Bangalore, India, and is listed as a full-time opportunity.

Location

The job is located in Bangalore, Karnataka, India.

Who Should Apply

This role is suitable for data engineers with 3 or more years of experience in data warehousing, analytics solutions, and production data pipelines. Candidates with strong SQL skills and experience in Spark, SparkSQL, Hive, Iceberg, Python, Java, Scala, or Airflow should consider applying.

It is also a good match for professionals who have worked with AWS, Snowflake, Redshift, APIs, data modeling, partitioning, and multi-source data integration. Candidates with additional exposure to AI agents, LLM applications, MCP, or AI-assisted development tools can bring relevant emerging skills.

Career Growth

This position can help data engineers strengthen their expertise across data pipelines, distributed processing, cloud platforms, analytics infrastructure, and enterprise data modeling. Working with multiple business functions can also provide broader understanding of how engineering teams turn organizational data into useful insights.

Exposure to AI-assisted development and LLM-powered data workflows can help engineers expand beyond traditional pipeline development. Experience with technologies such as Spark, Airflow, AWS, Snowflake, Redshift, and modern AI tooling can support longer-term growth toward senior data engineering, data platform, analytics engineering, and AI-enabled data roles.

Application Advice

Candidates should tailor their resumes around measurable data engineering experience. Highlight production data pipelines, SQL optimization, Spark or SparkSQL projects, data warehouse implementations, schema design, partitioning, orchestration, and cloud experience.

If you have worked with Airflow, AWS, Snowflake, Redshift, Hive, Iceberg, APIs, or multi-source ingestion, list these technologies clearly. Also mention examples where you improved pipeline reliability, data quality, query performance, automation, or engineering productivity.

For candidates with AI experience, include practical examples involving LLMs, agents, tool use, MCP, AI-assisted coding, or automated data workflows. Focus on real projects and outcomes rather than simply listing AI keywords.

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