AWS Systems Engineer at Openkyber New York, NY

Comunidade Metodista; New York (NY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Openkyber in New York, NY is seeking a Senior Data Engineer to design and implement scalable data pipelines, optimize data warehouses, and drive high‑quality analytics through robust ETL processes.

This contract role requires 12+ months, hybrid onsite in New York, with 3–4 days in the office weekly. Candidates must be local or from adjacent states and able to participate in F2F interviews.

Qualifications

• 7+ years of experience as Data Engineer with Data Pipeline Design and Development.
• Strong SQL skills and ability to write complex queries.
• Knowledge of big data architecture and data warehousing concepts.
• Experience with AWS and Snowflake is required.
• Proficiency in Python and shell scripting for data workflows.
• Familiarity with ETL tools and data quality checks.

Skills

Data pipeline design

SQL expert

Big data architecture

ETL concepts

Python

Shell scripting

AWS

Snowflake

Tools

SnowSQL

Job description

AWS Systems Engineer job at Openkyber. New York, NY.

Role: Senior Data Engineer

Locations: New York, NY (Hybrid Onsite)

Duration: 12+ Months Contract

F2F Interview Must

Note: Candidate needs to be in the office 3-4 Days every week. Local or candidates from adjacent states only.

Required Skills:

• Should have 7+ experience as Data Engineer with Data Pipeline Design, Development
• Good working knowledge of AWS, Snowflake, Python, Shell Scripting and any ETL tool
• Should be SQL expert and should be able to write complex SQL queries.
• Big Data architecture design
• In-depth understanding of Data Warehouse/ODS, ETL concepts.
• Performance tuning for large-scale system

Preferred Skills:

• Al-driven data processing exposure is plus
• Cloud data platforms (AWS/Azure)
• Programming language - strong proficiency in python, and SQL
• Cloud data warehousing- Snowflake, SnowSQL and query optimization,
• Data engineering Concepts - Knowledge of data ingestion, transformation, data quality checks and data security practices.
• Data modeling- Ability to design efficient data models for data warehousing environments.