Senior Data Engineer - AWS

Role Purpose:

Nomo is looking for a hands-on Senior Data Engineer to build and operate AWS-native data

platforms for a modern fintech environment.

This role is for someone who enjoys owning the full engineering lifecycle: infrastructure,

pipelines, security, observability, CI/CD, documentation, and production support. It is ideal for an

engineer who understands fundamentals. We build directly with native AWS services, open source tooling, Terraform, Python, and SQL.

What You’ll Do:

• Build and operate secure, scalable AWS-native data pipelines.
• Build event-driven architectures using EventBridge, SNS, SQS, Kinesis, and DynamoDB Streams.
• Own Terraform for data services and CI/CD dependencies.
• Own the medallion data lake structure with Hive and Apache Iceberg tables.
• Design table partitioning, deduplication, compaction, file sizing, retention, and optimization

settings.

• Build data models using dbt-core.
• Build and maintain third-party API integrations with credentials security, secret handling, and

credential rotation.

• Optimize Python and SQL for cost, latency, and operational reliability.
• Support data scientists with AI/ML infrastructure, curated datasets, pipelines, and lifecycle

support.

• Own logging, metrics, alerts, and operational visibility using CloudWatch and Datadog.

What We’re Looking For

• Strong production experience as a Data Engineer, Senior Data Engineer, Cloud Engineer, or

similar role.

• Ability to lead projects and work with other tech teams and external stakeholders.
• Strong ownership mindset across development, testing, deployment, monitoring,

documentation, and support.

• Strong hands-on experience with AWS-native data services.
• Good understanding of serverless, queues, streams, retries, DLQs, idempotency, and

replayability.

• Strong Terraform or Infrastructure as Code experience.
• Good understanding of IAM, least privilege, PII handling, and secure cloud engineering.

Nice to Have

• Fintech, banking, or regulated industry experience.
• Experience supporting ML or AI infrastructure on AWS.
• Experience with LakeFormation tag-based access control.
• Experience with Datadog alerting and incident response.
• Libraries such as awswrangler, DuckDB, and PyIceberg should click!

How You’ll Work

• AWS-native first: build close to the platform without depending on heavy SaaS abstractions.
• Hands-on: comfortable writing code, Terraform, IAM policies, tests, and operational

runbooks.

• Pragmatic: choose Lambda, Glue, DuckDB or PySpark based on the actual workload.
• Security-aware: treat PII, IAM, secrets, encryption, and auditability as core engineering

responsibilities.

• Performance-conscious: care about memory, file sizes, S3 I/O, partitions, cost, and query

performance.

• Collaborative: communicate clearly, define Done, test properly, and work well across teams.

What Success Looks Like

• Teams can move faster through reusable tooling, better deployment standards, and clearer

operational practices.

• Pipelines are reliable, secure, observable, and cost-aware.
• Data lake layers are well structured, governed, and optimized.
• Iceberg tables are maintained with clear partitioning, deduplication, and compaction logic.
• CI/CD, monitoring, documentation, and operational support are part of every delivery.

Why To Join Nomo

You will work on meaningful data engineering problems in a fintech environment where the team

owns the platform end to end. This is a role for someone who wants strong technical depth, and

the opportunity to shape AWS-native data engineering practices without relying on black-box

SaaS platforms.