Data Engineer

About this job

Ethos Life is a life insurance technology company that uses a three-sided platform to make life insurance accessible, instant, and online for consumers, agents, and carriers. As a Data Engineer joining the Data Platform team in Bangalore, you will build the internal infrastructure, services, and tooling that power the company's entire data stack. In this role, your daily work involves designing and operating distributed workflow systems like Temporal and Airflow, creating internal platform applications and config-driven UIs, writing shared Python libraries for other engineers, and administering data infrastructure such as Snowflake, dbt Cloud, and Kafka. You will serve internal users like analytics engineers, data scientists, and software engineers by delivering reliable developer-facing platforms, ensuring cost governance, and maintaining production systems end-to-end.

Responsibilities

• Build and operate internal data platforms, services, and distributed workflow systems including Temporal and Airflow.
• Write and maintain internal Python libraries and shared frameworks consumed by other engineering teams.
• Develop config-driven UIs and internal applications enabling users to author and run ingestion and transformation jobs.
• Administer Snowflake at depth, handling role hierarchy, RBAC, data masking, network policies, and resource monitors.
• Own cost governance, monitoring, alerting, and optimization across Snowflake, dbt Cloud, Airflow, and Kafka.
• Own production code end-to-end through design, code review, deployment, operation, and incident response.
• Support Analytics Engineering by building platform capabilities for orchestration, CI/CD, testing, and metadata.
• Collaborate with software engineering, analytics, business, product, and operations teams to meet platform requirements.

Requirements

• 4+ years of experience in software engineering.
• Strong proficiency in at least one programming language such as Go or Python.
• Experience building and maintaining backend systems, internal tools, or platform applications in production.
• Solid understanding of software engineering fundamentals like data structures, algorithms, concurrency, and system design.
• Familiarity with software development best practices including CI/CD, automated testing, code review, and observability.
• Proficiency in SQL and data modeling.
• Strong understanding of ETL processes, data warehousing, and data governance principles.
• Experience building and managing data-intensive or distributed applications and workflow-based systems.

Good to have

• Experience with workflow orchestration platforms such as Temporal.
• Experience building internal developer platforms or business-critical internal applications.
• Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker.
• Experience with Snowflake, Airflow, and dbt.
• Experience with cloud-native infrastructure, containerized deployments, and stream processing like Flink.

Who should apply

This role suits software engineers with a deep data background who enjoy building developer-facing platforms and operating distributed systems in production. Candidates who have experience managing data infrastructure, backend systems, and internal tooling will find this position a great fit. If you are passionate about the modern data stack and eager to support cross-functional engineering teams, you are encouraged to apply, even if your background does not match every single requirement.

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