Senior Data Engineer
ServiceTitan
Senior Data Engineer
Job Description
Ready to be a Titan?Play a pivotal role on our Data Platform team! As a Senior Data Engineer, you will help build and evolve our core data platform, working on high-impact features in a rapidly growing, high-scale environment.In this role, you will contribute to our next generation of data platform capabilities - supporting Semantic Modeling, improving Query Performance, and helping advance our Data Sharing capabilities. We build for perfection, use the most modern tools, have an amazing culture, and love to solve complex problems. If you share these values, you will find yourself in the perfect company.What you'll doImplement & Maintain: Build and maintain high-performance, fault-tolerant, and scalable systems to support enterprise-grade data and analytical products.Semantic & Shared Data Contributions: Contribute to the implementation of Semantic Layer and Data Share solutions, helping enable governed data access across the organization.Data Curation & Modeling: Help build clean semantic models using dbt MetricFlow to define standardized business metrics, and support data curation solutions that allow teams to quickly onboard new data sources.Performance Engineering: Implement and tune distributed storage and Query Performance techniques to improve platform efficiency and reduce latency across large datasets.Pipeline Engineering: Partner with cross-functional teams to extract, transform, and load data from a wide variety of transactional, streaming, and analytical sources.Code Quality: Write robust, well-tested, and maintainable code. Participate actively in code reviews and technical discussions.Observability & Quality: Build automation for monitoring, alerting, and measuring data quality to support high platform reliability.Operational Ownership: Participate in the on-call rotation to monitor system stability, respond to incidents, and support a culture of operational excellence.What you'll bringEducation: B.S. degree in Computer Science or a related field.Experience: 5+ years of hands-on experience in Software Engineering / Data Engineering roles working in high-traffic, highly available production environments.Semantic & Performance Exposure: Familiarity with Semantic Modeling concepts (dbt MetricFlow experience a plus) and experience troubleshooting and optimizing Query Performance.Core Modern Stack: Solid proficiency with cloud data warehouses (e.g., Snowflake), SQL, and data transformation tools like dbt.Programming Depth: Experience with Python, Spark, Java, Scala, or similar programming languages.Big Data & Streaming: Experience with Big Data technologies (Snowflake, Redshift, Hive/Hadoop, etc.); exposure to streaming/messaging platforms like Kafka or Kinesis is a plus.Operational Rigor: Experience participating in production on-call rotations and adhering to CI/CD guidelines.Be Human With Us: Being human isn’t about checking every box on a list. It’s about the experiences we have, people we meet, and the perspectives we share. So, if you have the skills but are hesitant to apply because of your background, apply anyway. We need amazing people like you to help us challenge the conventional and think differently about the problems that we’re solving. We’re in this together. Come be human, with us. Use of AI Technology:We use technology, including automated and AI-assisted tools, to support certain aspects of our recruitment process. These tools are designed to improve efficiency and enhance the candidate experience. AI tools are not used to make hiring decisions; all hiring decisions are made by our hiring teams.At ServiceTitan, we celebrate individuality and uniqueness. We believe that the convergence of fresh perspectives and experiences from all walks of life is what makes our product and culture so great. We do not discriminate against employees based on race, color, religion, sex, national origin, gender identity or expression, age, disability, sexual orientation, or any other characteristic protected by applicable laws.
Locations
- Karnataka, India Bengaluru
Skills Required
- Semantic Modeling conceptsintermediate
- cloud data warehousesintermediate
- Pythonintermediate
- Big Data technologiesintermediate
Required Qualifications
- Education: B.S. degree in Computer Science or a related field. (degree in computer science or a related field)
- Experience: 5+ years of hands-on experience in Software Engineering / Data Engineering roles working in high-traffic, highly available production environments. (experience, 5 years)
- Semantic & Performance Exposure: Familiarity with Semantic Modeling concepts (dbt MetricFlow experience a plus) and experience troubleshooting and optimizing Query Performance. (experience)
- Core Modern Stack: Solid proficiency with cloud data warehouses (e.g., Snowflake), SQL, and data transformation tools like dbt. (experience)
- Programming Depth: Experience with Python, Spark, Java, Scala, or similar programming languages. (experience)
- Big Data & Streaming: Experience with Big Data technologies (Snowflake, Redshift, Hive/Hadoop, etc.); exposure to streaming/messaging platforms like Kafka or Kinesis is a plus. (experience)
- Operational Rigor: Experience participating in production on-call rotations and adhering to CI/CD guidelines. (experience)
- Education: B.S. degree in Computer Science or a related field. (degree in computer science or a related field)
- Experience: 5+ years of hands-on experience in Software Engineering / Data Engineering roles working in high-traffic, highly available production environments. (experience, 5 years)
- Semantic & Performance Exposure: Familiarity with Semantic Modeling concepts (dbt MetricFlow experience a plus) and experience troubleshooting and optimizing Query Performance. (experience)
- Core Modern Stack: Solid proficiency with cloud data warehouses (e.g., Snowflake), SQL, and data transformation tools like dbt. (experience)
- Programming Depth: Experience with Python, Spark, Java, Scala, or similar programming languages. (experience)
- Big Data & Streaming: Experience with Big Data technologies (Snowflake, Redshift, Hive/Hadoop, etc.); exposure to streaming/messaging platforms like Kafka or Kinesis is a plus. (experience)
- Operational Rigor: Experience participating in production on-call rotations and adhering to CI/CD guidelines. (experience)
Responsibilities
- Implement & Maintain: Build and maintain high-performance, fault-tolerant, and scalable systems to support enterprise-grade data and analytical products.
- Semantic & Shared Data Contributions: Contribute to the implementation of Semantic Layer and Data Share solutions, helping enable governed data access across the organization.
- Data Curation & Modeling: Help build clean semantic models using dbt MetricFlow to define standardized business metrics, and support data curation solutions that allow teams to quickly onboard new data sources.
- Performance Engineering: Implement and tune distributed storage and Query Performance techniques to improve platform efficiency and reduce latency across large datasets.
- Pipeline Engineering: Partner with cross-functional teams to extract, transform, and load data from a wide variety of transactional, streaming, and analytical sources.
- Code Quality: Write robust, well-tested, and maintainable code. Participate actively in code reviews and technical discussions.
- Observability & Quality: Build automation for monitoring, alerting, and measuring data quality to support high platform reliability.
- Operational Ownership: Participate in the on-call rotation to monitor system stability, respond to incidents, and support a culture of operational excellence.
- Implement & Maintain: Build and maintain high-performance, fault-tolerant, and scalable systems to support enterprise-grade data and analytical products.
- Semantic & Shared Data Contributions: Contribute to the implementation of Semantic Layer and Data Share solutions, helping enable governed data access across the organization.
- Data Curation & Modeling: Help build clean semantic models using dbt MetricFlow to define standardized business metrics, and support data curation solutions that allow teams to quickly onboard new data sources.
- Performance Engineering: Implement and tune distributed storage and Query Performance techniques to improve platform efficiency and reduce latency across large datasets.
- Pipeline Engineering: Partner with cross-functional teams to extract, transform, and load data from a wide variety of transactional, streaming, and analytical sources.
- Code Quality: Write robust, well-tested, and maintainable code. Participate actively in code reviews and technical discussions.
- Observability & Quality: Build automation for monitoring, alerting, and measuring data quality to support high platform reliability.
- Operational Ownership: Participate in the on-call rotation to monitor system stability, respond to incidents, and support a culture of operational excellence.
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