AWS Data Engineer

BCforward; Plano (TX); Presencial; USD 130.000 - 180.000; Jornada completa

Descripción de la vacante

BCforward in Plano, TX is seeking a hands-on AWS Data Engineer to design, build, and operate our enterprise data lake and warehouse pipelines. You will leverage S3, Glue, Lambda, Lake Formation, and Snowflake to deliver secure, governed data, with IaC and CI/CD for every release.

AI tools like Copilot and Claude assist development. You will design scalable batch and event-driven pipelines, orchestrate with Step Functions, Lambda, and Control-M, and partner with analysts to turn requirements into

Formación

• 4–6 years of hands-on cloud data engineering experience.
• Strong experience with AWS Glue, S3, Lambda, Step Functions, and Lake Formation.
• Hands-on Snowflake experience and Python proficiency (Boto3 and AWS CLI).

Responsabilidades

• Design, build, and maintain scalable data pipelines on AWS.
• Orchestrate workflows with Step Functions and Lambda; schedule with Control-M.
• Develop Glue (Spark) and Python jobs for ingestion and transformation.
• Implement data lake security and governance with Lake Formation.
• Load and optimize data in Snowflake; support analytics with Athena/Redshift/EMR.
• Provision and manage infrastructure with Terraform; CI/CD via Bitbucket/Jenkins.
• Monitor pipelines with CloudWatch and Splunk; apply AI-assisted workflows.
• Collaborate with analysts and data scientists to deliver data products.

Conocimientos

Cloud data engineering

Python

AWS

Terraform

CI/CD

Git/Bitbucket

Jenkins

ServiceNow

Monitoring

Scheduler

Herramientas

AWS Glue

S3

Lambda

Step Functions

Lake Formation

Snowflake

Control-M

Terraform

Bitbucket

Jenkins

CloudWatch

Splunk

Athena

EMR

Redshift

EC2

Descripción del empleo

**Presently we are unable to sponsor and request applicants to apply who are authorized to work without sponsorship**

Location: Plano, TX (5 days onsite)

About the Role

We are looking for a hands-on AWS Data Engineer to design, build, and operate our enterprise data lake and warehouse pipelines. You will work with S3, Glue, Lambda, Step Functions, Lake Formation, and Snowflake to deliver secure, well-governed data, using infrastructure as code and CI/CD for every release. You will also use AI tools such as Copilot, Claude, and agentic workflows to speed up development and delivery.

Responsibilities

• Design, build, and maintain scalable batch and event-driven data pipelines on AWS.
• Orchestrate workflows with Step Functions and Lambda, and schedule jobs through Control-M.
• Develop Glue (Spark) and Python jobs for ingestion, transformation, and data movement.
• Implement data lake security and governance with Lake Formation (permissions, tagging, and cataloging).
• Load, model, and optimize data in Snowflake, and support analytics through Athena, Redshift, and EMR where needed.
• Provision and manage infrastructure with Terraform.
• Build and maintain CI/CD pipelines using Bitbucket/Git and Jenkins, and manage releases through ServiceNow change processes.
• Monitor, troubleshoot, and tune pipelines using CloudWatch and Splunk.
• Apply AI-assisted and agentic workflows to coding, testing, documentation, and operations.
• Work with analysts, data scientists, and business stakeholders to turn requirements into reliable data products.

Required Qualifications

• 4-6 years of hands-on cloud data engineering experience.
• Strong experience with AWS Glue, S3, Lambda, Step Functions, and Lake Formation.
• Hands-on Snowflake experience.
• Proficiency in Python, including Boto3 and the AWS CLI.
• Experience with Terraform and CI/CD deployment into AWS.
• Working knowledge of Git/Bitbucket, Jenkins, and ServiceNow release processes.
• Experience with CloudWatch and Splunk for logging and tracing.
• Familiarity with at least one scheduler (Control-M preferred).
• Working familiarity with AI tools (Copilot, Claude) and agentic workflows.

Preferred Qualifications

• Databricks experience (Unity Catalog) and Snowflake Horizon Catalog knowledge.
• Experience with Athena, S3 Select, EMR, Redshift, and EC2.
• Strong Spark and distributed data processing fundamentals.