About The Role :

The Data Platform Engineer is responsible for enabling data practitioners such as data engineers, business analysts and data scientists to self-serve on a robust and scalable platform infrastructure by designing and building the right tools and reusable data platform frameworks.

In This Role, You Will :

- Design, develop, and maintain the storage, processing, orchestration, cataloging and governance components of a scalable and secure data platform.

- Build the tools, libraries, and services that allow other teams to own and manage their own pipelines and workflows independently.

- Provide self-service infrastructure (e.g., templates, SDKs, CI/CD patterns, DBT macros) to support repeatable and consistent data engineering practices.

- Implement and manage data platform components : orchestration frameworks, data catalog, access control layers, and metadata systems.

- Collaborate with stakeholders to define SLAs, monitoring, and observability across the data stack.

- Champion infrastructure as code, automation, and standardization across the platform.

- Ensure data security, compliance, and cost efficiency across environments.

- Mentor and guide other data platform associates with solutioning, code reviews and best practices adoption.

Minimum Qualifications :

- We realize applying for jobs can feel daunting at times. Even if you dont check all the boxes in the job description, we encourage you to apply anyway.

- 6+ years of experience in Data/Infrastructure Engineering, with at least 3 years focused on building self-service platforms.

- Proficiency in Python, SQL and experience building reusable templates and frameworks.

- Deep understanding of cloud data resources in AWS (S3, EKS, Glue, Athena etc.).

- Expertise in building and supporting solutions on Snowflake.

- Experience in setting up integrations or connecting tools (for e.g. Snowflake - Power BI, Snowflake - AWS services etc.)

- Hands-on experience with orchestration frameworks (Airflow, Prefect etc.).

- Experience with distributed systems and data processing frameworks (e.g., Apache Spark).

- Comfortable building and debugging CI/CD, infrastructure as code (Terraform), and GitOps practices.

- Familiarity with Kubernetes, Docker, and container-based deployment models.

- Demonstrated capability in security & governance : RBAC, masking, SSO (Okta), secrets management and audit logging.

What Can Help Your Application Stand Out :

- Experience in Data Science productionisation (MLOps).

- Active contributions to open-source data projects.

- Experience implementing Apache Iceberg for open-table formats.

- Experience implementing Kafka, Kinesis, or Flink for streaming architectures.

- Exposure to observability tools (Datadog, splunk etc.).