Data Engineer

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.