Data Platform Engineer

Role overview Build and operate reliable data pipelines and platform services across ingestion, transformation, orchestration, and API integration. You will combine practical software engineering with infrastructure automation, observability, security, and testing to support dependable product and analytics data flows. The position is suited to an engineer who communicates clearly and can take ownership of production systems.

Responsibilities - Develop and operate batch and change-driven ingestion pipelines, including scheduling, schema versioning, retries, replays, and failure handling. - Implement tested transformations and merges, create models for product and analytics use, and tune SQL queries. - Build or integrate REST APIs with defined contracts, pagination, authentication, validation, rate limiting, idempotency, and auditing. - Manage cloud infrastructure with Terraform, implement CI/CD with automated promotion, and write shell scripts for operational workflows. - Add logs, metrics, traces, actionable alerts, runbooks, and service-level tracking to improve reliability. - Apply secure access, secrets handling, encryption, code tests, SQL tests, integration tests, and data-quality checks.

Requirements - At least four years of experience in data engineering, backend engineering, or DevOps. - Strong SQL capability and solid relational-database fundamentals, including experience with systems such as PostgreSQL, SQL Server, or MySQL. - Proficiency with Python for data or service work and with Bash or PowerShell scripting. - Experience with Terraform, infrastructure as code, CI/CD, and automated testing. - Azure experience preferred; strong AWS experience is acceptable. - Familiarity with ingestion, orchestration, schema evolution, resilient processing, REST APIs, OAuth2/OIDC/JWT, and production observability.

Nice to have - Experience with Prefect; Airflow or Dagster experience is also relevant. - Knowledge of compliance, dimensional modeling, BI, data governance, streaming, message queues, Parquet, or cloud monitoring tools. - Ability to prototype simple operational views and use AI coding assistants with sound engineering judgment.

Benefits and work setup - Remote contract engagement, with preference for candidates in India or Latin America. - Expected duration of at least 10 months, with possible extension or transition to full-time work. - Flexible schedule with time-zone overlap for key meetings and agreed virtual on-call windows. - Hands-on ownership of modern data stacks, with exposure to reliability, observability, Terraform, cloud platforms, and workflow orchestration.