Senior Data Engineer

ABCorp NA Inc.•Boston, MA

9d•Onsite

About The Position

ABCorp is seeking a Senior Data Engineer to own the data infrastructure and pipeline layer that powers our business and operational analytics. Sitting at the foundation of our data organization, this role is responsible for building the reliable, scalable pipelines that move data from our manufacturing systems, ERP platforms, financial systems, and operational tools into our modern data warehouse — where it fuels analytics, reporting, and product decisions across the company. You will serve as the architect of our operational data layer, modernizing how we collect, move, and model business-critical data and building the foundation that the rest of the data organization depends on. The ideal candidate brings deep data engineering expertise, a systems-oriented mindset, and a passion for building infrastructure that simply works — reliably, at scale, and with the rigor of a production software engineer. As a key partner to our Engineering, IT, and Analytics teams, you will help build the data backbone that supports products and decisions reaching millions of people worldwide across fintech, commercial, healthcare, and government industries — and mentor the engineers around you to raise the bar for how the organization works with data. This is an in-person role with our team based in Boston, MA. Please note there is no Visa Sponsorship for this position.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
5+ years of experience in data engineering, with a track record of building production-grade ELT/ETL pipelines, data warehouse models, and data infrastructure in a modern cloud environment.
Expert-level SQL proficiency including complex query optimization, performance tuning, and deep knowledge of data warehouse query patterns.
Hands-on experience with dbt for data modeling, transformation, and testing — including lineage management, CI testing, documentation, and production-grade model ownership.
Strong experience with cloud data warehouses, primarily Azure Synapse Analytics, and deep understanding of warehouse architecture, ELT patterns, and performance optimization.
Strong Python proficiency for pipeline development, automation, and data engineering tasks — applied with software engineering discipline, not just scripting.
Experience with Azure Data Factory (ADF) for pipeline orchestration, and familiarity with CI/CD and infrastructure-as-code practices applied to data pipelines.
Deep experience with Azure data infrastructure — including Azure Synapse Analytics, Azure Databricks, Azure Data Lake Storage (ADLS), Azure Data Factory, Azure DevOps, and Azure Monitor.
Proven ability to own complex, multi-month data engineering projects with minimal guidance — from architecture through to production deployment.
Self-starter mindset with strong ownership, engineering rigor, and comfort operating in a fast-paced environment where you help define how the data platform grows.
Ability to work onsite in Boston, MA.

Nice To Haves

Experience with ERP systems and operational data — familiarity with platforms such as Infor Visual, SQL Server, SAP, or similar enterprise business systems is a strong plus.
Familiarity with event streaming systems, CDC pipelines, or large-scale schema migration projects is a plus.

Responsibilities

Design, build, and own end-to-end data pipelines that move data from operational systems — including ERP (Infor Visual), CRM, billing, and manufacturing platforms — into Snowflake with reliability, observability, and testability built in from the start.
Lead the integration and modernization of data from legacy systems including Infor Visual (ERP) and SQL Server databases into our modern data warehouse, treating every migration as an opportunity to improve data quality and architecture.
Build and maintain dbt models that form the raw-to-staging foundational layer of our data warehouse — clean, tested, documented, and reliable sources that the Analytics Engineering team builds metrics and insights on top of.
Design and implement pipeline orchestration using Azure Data Factory (ADF), applying CI/CD practices, data contract testing, and infrastructure-as-code principles to ensure pipelines are maintainable and production-grade.
Identify and execute opportunities to modernize legacy data processes — replacing manual exports, fragile SQL scripts, and ad-hoc reporting workflows with properly engineered, maintainable pipelines.
Collaborate closely with the Analytics Engineering team to define the upstream data layer that their downstream models, metrics, and dashboards are built on — ensuring clean handoffs, clear data contracts, and shared understanding of data lineage.
Implement and own data quality monitoring, alerting, and observability practices that catch pipeline issues and data anomalies before they reach downstream consumers.
Manage and evolve the data platform infrastructure on Azure — including Azure Synapse Analytics, Azure Databricks, ADLS, compute resources, access controls, and cost optimization.