Data Engineer – Intermediate

Ampcus Inc; New York (NY); On-site; USD 80,000 - 110,000; Full time

Job summary

Ampcus Inc. in New York is seeking an Intermediate Data Engineer to design and manage scalable ETL pipelines for analytics. The role involves working with AWS services and optimizing data processes within a centralized data lake and Snowflake environment. Candidates should have strong experience with data lakes, ETL pipelines, and proficiency in PySpark and Apache Spark. This position will require onsite work in Manhattan West, NY.

Qualifications

• Strong experience with data lake architectures and large-scale data processing.
• Hands-on experience with AWS services for data management.
• Proven expertise in building ETL pipelines for analytics.

Responsibilities

• Design, develop, and maintain robust ETL pipelines.
• Automate data processing and analytical workflows.
• Implement orchestration of data pipelines using Control‑M and Apache Airflow.
• Develop data transformation logic using PySpark and Spark.

Skills

Data lake architectures

AWS services (S3, EC2, EMR, Glue)

ETL pipelines

Snowflake

Control-M and Apache Airflow

PySpark

Apache Spark using Java

Data modeling and performance tuning

Job description

Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are in search of a highly motivated candidate to join our talented Team.

Job Title

Data Engineer – Intermediate

Location

Manhattan West, NY – Onsite

Job Description

We are seeking a skilled Data Engineer to design, build, and manage scalable ETL pipelines supporting a centralized data lake and Snowflake data warehouse. The role focuses on automating data ingestion, transformation, and aggregation workflows to enable reliable analytics and data-driven decision-making.

Key Responsibilities

• Design, develop, and maintain robust ETL pipelines for ingesting data into the enterprise data lake and Snowflake environment.
• Automate data processing, aggregation, and analytical workflows to improve data availability and performance.
• Implement and manage orchestration and scheduling of data pipelines using Control‑M and Apache Airflow.
• Develop scalable data transformation logic using PySpark and Apache Spark (Java).
• Work with large, structured and semi-structured datasets on AWS infrastructure.
• Ensure data quality, integrity, and reliability across data pipelines.
• Optimize data pipelines for performance, cost, and scalability.
• Collaborate with analytics, data science, and business teams to understand data requirements.
• Monitor, troubleshoot, and resolve pipeline failures and performance bottlenecks.
• Follow best practices for data engineering, security, and documentation.

Required Skills & Qualifications

• Strong experience with data lake architectures and large-scale data processing.
• Hands‑on experience with AWS services (e.g., S3, EC2, EMR, Glue, or related).
• Proven expertise in building ETL pipelines for analytics and reporting use cases.
• Solid working knowledge of Snowflake, including data loading, transformations, and performance optimization.
• Experience with workflow automation and scheduling tools such as Control‑M and Apache Airflow.
• Proficiency in PySpark for distributed data processing.
• Strong programming experience with Apache Spark using Java.
• Good understanding of data modeling, partitioning, and performance tuning concepts.

Preferred Qualifications (Nice To Have)

• Exposure to CI/CD practices for data pipelines.
• Experience working in Agile or DevOps environments.

Ampcus is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veterans or individuals with disabilities.