Senior Data Engineer

BMC Software; Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

BMC Software is seeking an experienced Senior Data Engineer to design, build, and scale data platforms and pipelines powering analytics, reporting, and AI initiatives. You will collaborate with analytics, data science, and business teams to deliver trusted data at scale.

The role requires strong SQL and Python/Scala/Java, hands-on experience with Snowflake, BigQuery, Redshift, Databricks, and Airflow/dbt, and a passion for data quality and governance.

Qualifications

• 8+ years of data engineering experience in B2B SaaS or large-scale tech environments.

Responsibilities

• Design, build, and maintain scalable data pipelines and integrations across internal and external systems.

Skills

Data engineering

SQL

Python / Scala / Java

ETL / ELT Development

Data Modeling

Cloud Data Platforms

Data Warehousing

Pipeline Orchestration

AI / ML data pipelines

Data Quality & Governance

Performance Optimization

Analytics Enablement

Cross-Functional Collaboration

Education

Bachelor's degree in Computer Science or related field

Tools

Snowflake

BigQuery

Redshift

Databricks

Airflow

dbt

Fivetran

Job description

Job Summary

We are seeking an experienced Senior Data Engineer to design, build, and scale modern data platforms and pipelines that power Agentic-AI, analytics, reporting, machine learning, and data-driven decision-making across the business. This role is ideal for someone with a strong background in B2B SaaS or enterprise technology environments, where data is critical to driving sales & product insights, revenue operations, customer success, and executive reporting.

The ideal candidate brings deep expertise in data architecture, ELT/ETL development, cloud data platforms, data modeling, and data quality, along with a strong understanding of how to build reliable, scalable, and governed data solutions in fast-paced technology organizations. This person will partner closely with analytics, data science, business teams, and platform stakeholders to ensure trusted data is delivered efficiently and at scale.

Responsibilities

• Design, build, and maintain scalable data pipelines and integrations across internal and external systems.
• Develop and optimize data solutions that support analytics, BI, machine learning, AI/Agentic AI, operational reporting, and self-service data access.
• Build robust ELT/ETL workflows to ingest and transform data from systems such as CRM, product telemetry, marketing, finance, support, and subscription platforms.
• Design and maintain clean, scalable, and well-documented data models for enterprise reporting and analytics use cases.
• Improve data reliability, quality, observability, lineage, and performance across the data platform.
• Design and maintain scalable data pipelines and data services that enable AI/ML and GenAI use cases, including model-ready datasets, feature pipelines, and support for retrieval-based and intelligent application workflows.
• Partner with analytics engineers, data scientists, architects, and business stakeholders to translate requirements into production-grade data solutions.
• Ensure data pipelines are secure, governed, and aligned with enterprise data standards and best practices.
• Optimize data processing and storage for performance, cost, scalability, and maintainability.
• Support near real-time and batch data processing patterns as required by business use cases.
• Contribute to the design of the overall data architecture, including ingestion, transformation, orchestration, storage, semantic modeling, and data serving layers.
• Troubleshoot pipeline issues, resolve data inconsistencies, and drive root-cause remediation.
• Contribute to engineering standards, code quality, and team best practices.

Qualifications

• Bachelors degree in Computer Science, Engineering, Information Systems, or a related technical field.
• 8+ years of experience in data engineering, preferably in B2B SaaS or large-scale technology companies.
• Strong experience designing and building scalable data pipelines and cloud-based data platforms.
• Advanced proficiency in SQL and strong programming skills in Python, Scala, or Java.
• Hands-on experience with modern cloud data platforms such as Snowflake, BigQuery, Redshift, Databricks, or similar.
• Experience with data orchestration and transformation tools such as Airflow, dbt, Informatica, Fivetran, or equivalent.
• Strong understanding of data modeling, including dimensional modeling and analytics-oriented schema design.
• Experience working with data domains such as product analytics, GTM analytics, customer success, finance, or subscription analytics.
• Experience building and managing data pipelines that support AI/ML workloads, including feature preparation, model-ready datasets, and scalable ingestion of structured and unstructured data.
• Familiarity with modern data architectures for AI/GenAI use cases, including vector-ready data pipelines, metadata management, and support for retrieval and inference workflows.
• Experience with CI/CD, infrastructure-as-code, and engineering best practices in modern data environments.
• Exposure to semantic layers, governed metrics, and enterprise data product architectures.
• Experience working with structured and semi-structured data at scale.
• Familiarity with data quality, observability, lineage, and governance best practices.
• Experience in Agile or product-oriented delivery environments.
• Strong problem-solving skills and ability to work across technical and business teams.
• Strong communication skills and ability to explain technical concepts clearly to non-technical stakeholders.

Core Skills

• Data Engineering
• SQL
• Python / Scala / Java
• ETL / ELT Development
• Data Modeling
• Cloud Data Platforms
• Data Warehousing
• Pipeline Orchestration
• Data Engineering for AI / ML
• AI Data Pipelines and Feature Engineering
• Data Quality and Governance
• Performance Optimization
• Analytics Enablement
• Cross-Functional Collaboration

What Success Looks Like

Success in this role means building trusted, scalable, and efficient data foundations that enable the business to move faster with confidence. The successful candidate will improve data accessibility, reduce pipeline failures, strengthen data quality, and create the technical foundation needed for analytics, AI, reporting, and operational decision-making across the company.