Data Engineer & Agentic AI Lead

Vaco; New York (NY); On-site; USD 90,000 - 130,000; Full time

Job summary

Vaco in New York is seeking a hands-on Data Engineer to build a modern, centralized data platform. You will transform raw data into clean, structured datasets and own data pipelines end-to-end.

The role emphasizes production data pipelines, SQL, data transformation and modeling, and collaboration with stakeholders to support Power BI, analytics, and AI initiatives. You will work with large datasets and data lakes, and you will independently drive projects to deliver trusted data for business

Qualifications

• Hands-on data engineering and ETL experience.
• Experience designing and building production data pipelines.
• Strong SQL skills.
• Experience with data transformation and data modeling.
• Experience with large, complex datasets from multiple sources.
• Able to turn messy source data into clean datasets.
• Familiar with data lakes, databases, and modern data architectures.
• Ability to translate business requirements into scalable solutions.
• Independent and able to own projects end-to-end.

Responsibilities

• Design, build, and maintain scalable end-to-end ETL/data pipelines.
• Ingest data from multiple internal and external sources.
• Clean, transform, validate, and structure large volumes of data.
• Handle diverse formats like Excel, CSV, JSON, ORC, Parquet.
• Load and organize transformed data in databases and data lakes.
• Design scalable data models based on business requirements.
• Build data and semantic layers for reporting and analytics.
• Help develop a centralized enterprise data platform.
• Work with datasets that can reach billions of rows.
• Prepare trusted data for Power BI, analytics, and AI applications.
• Collaborate with business and technical stakeholders to define data structures.

Skills

Data engineering

ETL pipelines

SQL

Data transformation

Data modeling

Job description

Data Engineer

Position Overview

We are seeking a hands-on Data Engineer to help build and evolve a modern, centralized data platform. This role will work with large volumes of data coming from a variety of internal and external sources, transforming raw and sometimes inconsistent information into clean, structured, reliable datasets that can be used across the business.

The ideal candidate is a strong data engineer at their core: someone who has personally built ETL/data pipelines, understands data transformation and modeling, and is comfortable working independently with complex datasets. This is not a reporting-focused or Data Analyst position.

Responsibilities

• Design, build, and maintain scalable end-to-end ETL/data pipelines
• Ingest data from a variety of internal and external sources
• Clean, transform, validate, and structure large volumes of raw data
• Work with multiple data formats, including Excel, CSV, JSON, ORC, and Parquet
• Load and organize transformed data within database and data lake environments
• Design scalable data models based on business and analytical requirements
• Build the underlying data and semantic layers used for reporting and analytics
• Help develop and expand a centralized enterprise data platform
• Work with datasets that can reach billions of rows
• Prepare trusted, well-structured data for downstream use across Power BI, advanced analytics, and future AI applications
• Partner with business and technical stakeholders to understand how data needs to be structured and consumed

Required Qualifications

• Strong hands-on data engineering and ETL experience
• Demonstrated experience personally designing and building production data pipelines
• Strong SQL skills
• Strong experience with data transformation and data modeling
• Experience working with large, complex datasets from multiple sources
• Ability to take messy or unstructured source data and turn it into clean, usable datasets
• Experience working with data lakes, databases, and modern data architectures
• Ability to translate business data requirements into scalable technical solutions
• Comfortable working independently and owning projects from requirements through implementation