About the job

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 noticesthem.
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.

Qualifications

5+ years hands-on experience as a Data Engineeron 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.

Responsibilities

About the job

Role Overview

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 noticesthem.
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.

Qualifications

5+ years hands-on experience as a Data Engineeron 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.