Data Engineer

Business Needs Inc; California (MO); Remote; USD 120,000 - 180,000; Full time

Job summary

Indeed is seeking a Data Engineer to design, build, and maintain end-to-end ELT/ETL pipelines in cloud environments. Strong SQL and Python are required, along with orchestration tools such as dbt, Airflow, or Dagster, and AWS data services.

You''ll work on data models and large-scale processing, optimize pipelines for performance and cost, and collaborate with Engineering and Finance to deliver cost visibility and usage insights on a 12-month contract with remote work.

Qualifications

• End-to-end ELT/ETL pipelines in cloud environments.
• Strong SQL and Python proficiency.
• Experience with dbt, Airflow or Dagster.
• AWS data ecosystem experience (Glue, Athena, Aurora).
• Experience with Snowflake and data modeling at scale.
• CI/CD, version control, Terraform and REST API integrations.
• Ability to optimize pipelines for performance and cost and to validate data.

Responsibilities

• Design, build, and maintain the Cloud Economics team's data pipelines and automation systems.
• Build scalable data pipelines and datasets for cloud cost visibility and performance insights.
• Design data models and ingestion frameworks for telemetry and usage data.
• Develop tooling enabling cost-aware decision-making for stakeholders.
• Optimize data systems for performance, reliability, and cost efficiency.
• Partner with Engineering and Finance to support cost optimization initiatives.
• Ensure data quality, validation, and auditability across end-to-end workflows.

Skills

ELT/ETL pipelines

SQL

Python

dbt

Airflow

Dagster

AWS data services

AWS Glue

Amazon Athena

Amazon Aurora

Snowflake

Data modeling

CI/CD

Terraform

REST APIs

Data validation

Troubleshooting production pipelines

Tools

AWS Glue

Amazon Athena

Amazon Aurora

Snowflake

Terraform

Job description

Company: Indeed

Position: Data Engineer

Location: Remote

Length: 12-month Contract

Job Description

Must-Have Skills: Strong Data Engineering foundation with proven experience building and maintaining end-to-end ELT/ETL pipelines in cloud environments. Strong SQL and Python. Experience with orchestration tools such as dbt, Airflow, or Dagster. AWS data ecosystem experience, including AWS Glue, Amazon Athena, and Amazon Aurora. Experience with Snowflake. Strong data modeling and large-scale data processing experience. Experience with CI/CD, version control, Infrastructure as Code (Terraform), and REST API integrations. Demonstrated ability to optimize data pipelines for performance and cost. Strong analytical skills with experience in data validation, auditing, and troubleshooting production pipelines.

Nice-to-Have Skills: FinOps Foundation Certification (Practitioner or Engineer). Experience with cloud cost optimization, tagging strategies, or cost monitoring. Experience with AWS and GCP. Experience with Datadog. Background in data platform or shared infrastructure engineering. Scala is a plus.

Design, build, and maintain the Cloud Economics team's data pipelines and automation systems. Build and maintain scalable data pipelines and datasets that power cloud cost visibility, attribution, and performance insights. Design and implement data models and ingestion frameworks to support large-scale telemetry and usage data. Develop tooling that enables cost-aware decision-making across Product, Engineering, and Finance stakeholders. Optimize data systems for performance, reliability, and cost efficiency (query tuning, storage strategies, compute optimization). Partner with Engineering and Finance teams to support cost optimization initiatives and usage-based insights. Ensure data quality, validation, and auditability across end-to-end data workflows.