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# Data Engineer

- **Company:** FocusKPI, Inc.
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Salary:** $93,600.0 - $114,400.0
- **Contract:** Temporary contract
- **Skills:** Airflow, Amazon Web Services, Amazon S3, Big Data, Software Documentation, Code Review, Continuous Integration, Data Validation, Data Infrastructure, Extract Transform Load (ETL), Data Security, Amazon DynamoDB, Github, Apache Hive, Information Lifecycle Management, Python (Programming Language), Scala (Programming Language), Amazon Simple Notification Service (SNS), Apache Spark, Build Management, Infrastructure Automation Frameworks, Apache Kafka, Functional Programming, Amazon Simple Queue Service (SQS), Terraform, Looker Analytics, Data Pipelines, Amazon Elastic Mapreduce (EMR), Docker, Amazon Redshift
- **Published:** June 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=fbdbfc37bf1ca190

## About the Role

Do you have experience in Terraform?, Do you have a Bachelor's degree?, * Bachelor's degree in the Computer field, which requires logical, creative thinking, STEM majors, MS degree preferred

 * 5+ years of industry experience are required
 * Team's Tech Stack: Spark, Scala, Python, DBT, Airflow, Kafka, AWS EMR, Hive, Redshift, Docker, Lambda, SNS, SQS, S3, Looker, DynamoDB, GitHub Actions, Terraform
 * Experience with data models
 * Experience with CI/CD
 * Experience collaborating with and understanding the needs of stakeholders from a variety of business functions, including product, marketing, finance, and so on
 * Experience in handling large-scale data with efficiency
 * Strong analytical and problem-solving skills
 * Experience in Financial Services or FinTech preferred

**No C2C resumes are considered**

## Description

* Architect and scale modern data platforms on AWS - Design and build robust, scalable Spark-based ETL/ELT pipelines and data workflows supporting batch and real-time use cases.
 * Own end-to-end data lifecycle management - From ingestion and transformation to modeling, orchestration, and serving layers.
 * Ensure data quality, governance, and reliability - Implement testing frameworks, observability, monitoring, lineage, and data validation practices.
 * Drive platform optimization and cost efficiency - Continuously improve performance, scalability, and cloud cost management across the data ecosystem.
 * Establish engineering best practices - CI/CD, Infrastructure as Code, code reviews, documentation standards, and secure data handling.
 * Partner cross-functionally with technical and business stakeholders to translate data needs into reliable, production-grade solutions


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