About Grain
Grain is a fast-growing fintech startup offering cross-currency solutions tailored for software platforms and marketplaces. We’re backed by leading venture capital firms and prominent financial institutions. At Grain, we foster a collaborative, high-impact culture where every team member plays a direct role in shaping our success.
Role Overview
We're looking for a talented Senior Data Engineer to help build Grain's data platform from the ground up.
The ideal candidate takes end-to-end ownership - from understanding business requirements to shipping reliable pipelines in production. This is a hands-on role at a critical moment: we're building our data platform from the ground up, which means high autonomy, direct stakeholder access, and architecture decisions that stick.
Responsibilities
- Own the data development process end-to-end: business understanding, design, implementation, QA, and production maintenance.
- Design, build, and operate our cloud data platform - ingestion pipelines, streaming and batch processing, and a structured analytical layer serving Risk, Finance, Product and other stakeholders.
- Consolidate diverse data sources (internal databases, external FX rate feeds, bank files, third-party APIs) into a governed, reliable analytical layer.
- Implement and maintain CI/CD, observability, and infrastructure-as-code practices - DEV/QA/PROD parity, pipeline monitoring, alerting on data quality issues before the business notices them.
- Build the foundations of an ML feature platform, enabling data scientists to focus on modeling rather than pipeline plumbing.
- Ensure data quality and integrity across ETL processes - owning what happens when checks fail, not just that they run.
- Collaborate with analysts, data scientists, and business stakeholders to translate business requirements into data models and pipeline logic.
Requirements
- 5+ years hands-on experience as a Data Engineer on AWS.
- Strong Python and SQL - clean, testable, production-grade code.
- Proven experience building and operating data pipelines using DMS, Glue, and Airflow (MWAA).
- Real streaming experience - Kinesis or Kafka in production, not just local setup. Knows what consumer lag means and how to debug it.
- Experience with CDC architectures and schema evolution challenges in production environments.
- Experience with Snowflake or a comparable analytical database.
- Solid understanding of data modeling, cloud cost awareness, and performance tuning.
- Strong problem-solving instincts: can work with ambiguous requirements, makes reasonable decisions and documents them.
- Good communicator - comfortable talking directly to non-technical stakeholders.
Advantage
- Apache Iceberg in production (schema evolution, compaction, time travel).
- Exposure to financial data domains - FX, treasury, trade reconciliation.
- Experience with dbt for transformation layer modeling.
- Familiarity with Terraform for infrastructure-as-code.
- Comfortable leveraging AI development tools such as Cursor, Claude Code, or GitHub Copilot to improve engineering productivity.