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Open nowFirst seen 43 hours ago

Data Engineer

grain/4a.0003 open roles

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. grain/4a.000 postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: first seen 43 hours ago

grain/4a.000 median: 1 days open

The posting

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.
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