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# Data Engineer

- **Company:** C&G Consulting Services
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Salary:** $124,800.0 - $156,000.0
- **Contract:** Temporary to permanent
- **Skills:** Query Performance, Artificial Intelligence, Airflow, Amazon Web Services, User Authentication, Microsoft Azure, Big Data, BigQuery, Business Systems, Cloud Database, Code Review, Microsoft Management Console, Data Cleansing, Information Engineering, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Structures, Data Warehousing, Database Queries, Dimensional Modeling, Python (Programming Language), Enterprise Messaging Systems, Query Optimization, Student Information Systems, SQL Databases, Data Streaming, Systems Integration, Talend, Data Logging, Feature Store, Scripting, Macros, Data Ingestion, Large Language Models, Snowflake, Git, Containerization, Apache Kafka, Restful APIs, Software Version Control, Data Pipelines, Docker, Amazon Redshift, Web Api
- **Published:** October 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=507f0d49b16c3784

## About the Role

Must be US Citizen or Green CardData Engineer
Must be able to interview and work onsite

Must have excellent comm skills

Must emphasize more of the transformation/data modeling side of things., Required:

4-7 years of experience in a data engineering or similar role

Strong proficiency in Python for scripting, automation, and pipeline development

Hands-on experience building and orchestrating pipelines with Apache Airflow (DAG design, scheduling, sensors, operators)

Deep, hands-on experience with dbt for data transformation and modeling (models, tests, macros, documentation, incremental models)

Demonstrated experience designing star schemas and other dimensional models (facts, dimensions, slowly changing dimensions, grain decisions)

Strong SQL skills, including query optimization and working with large datasets

Experience consuming from and integrating with third-party APIs (REST, authentication methods, error handling)

Experience with cloud platforms (AWS, GCP, or Azure) and cloud data warehouses (e.g.,

Snowflake, BigQuery, Redshift). AWS and Redshift preferred.

Familiarity with version control (Git) and collaborative development workflows

Strong problem-solving skills and attention to data quality and accuracy

Nice to Have:

Experience ingesting or integrating data from Student Information Systems, such as eSchoolData or similar platforms

Experience building data pipelines or infrastructure to support AI/ML products or features

(e.g., feature stores, LLM-powered applications, or data preparation for model training/inference)

Familiarity with containerization (Docker) and CI/CD pipelines

Experience with streaming data tools (Kafka, Kinesis) or other messaging systems

Experience with Talend Management Console and Talend Studio

## Description

Experienced Data Engineer (4-7 years) to help design, build, and maintain the data pipelines and infrastructure that power analytics and decision-making across our organization. You'll play a key role in modeling our data warehouse using dimensional modeling best practices and building robust transformation pipelines that analysts and data scientists depend on daily. You'll work closely with data analysts, data scientists, and software engineers to ensure our data is reliable, well-modeled, and accessible.

What You'll Do

Design, build, and maintain scalable ETL/ELT pipelines using Python, Apache Airflow, and dbt

Design and implement dimensional data models (star schemas, fact and dimension tables, slowly changing dimensions) that support scalable, performant analytics

Write efficient, well-structured SQL to transform and model data for analytics and reporting, with an eye toward query performance and warehouse cost

Integrate data from internal and third-party APIs, handling authentication, pagination, rate limits, and error handling

Build and maintain ingestion pipelines from core business systems, including CRM, HR/HCM platforms and Student Information Systems

Monitor and troubleshoot data pipelines, proactively identifying and resolving data quality issues

Schedule, orchestrate, and monitor workflows in Airflow, ensuring reliability and observability (alerting, logging, retries)

Collaborate with analysts, data scientists, and stakeholders to understand data needs and translate them into well-modeled, reusable data structures

Write clean, tested, and documented code; contribute to code reviews and engineering best practices

Participate in on-call rotation or incident response for critical data pipeline issues

Continuously improve data infrastructure for performance, cost, and maintainability