Senior Data Engineer

DeWinter Group; Boston (MA); Hybrid; USD 140,000 - 190,000; Full time

Job summary

DeWinter Financial Services Partner in Boston, MA is seeking a senior data engineer to design, build, and evolve a next-generation data platform. This hands-on role focuses on scalable data pipelines that move data from operational systems into a centralized lakehouse built on Kafka, Iceberg, AWS, and Snowflake.

You will work on large-scale ingestion, streaming architectures, data quality, and platform capabilities, collaborating with platform engineering, infrastructure, security, and

Qualifications

• 5+ years of data engineering or related field.
• Hands-on experience building production data pipelines.
• Experience with Kafka and cloud data platforms (AWS).

Responsibilities

• Design, build, and scale data pipelines for high-volume ingestion.
• Develop streaming and batch data solutions.
• Move data from source systems (SQL Server, PostgreSQL, DynamoDB) into central data platform.
• Contribute to Kafka, Iceberg, and cloud platform initiatives.
• Collaborate with platform engineering, infra, security, and apps teams.
• Improve data quality, observability, and reliability across the platform.
• Leverage AI-assisted development tools in the workflow.
• Work with multi-terabyte tables and large event streams.

Skills

Data pipelines

Kafka

Python

AWS

Iceberg

Snowflake

Databricks

Production data pipelines

Tools

SQL Server

PostgreSQL

DynamoDB

Job description

This role is with a DeWinter Financial Services Partner

Boston, MA - Hybrid Role - We are targeting local candidates that can be in the Boston office 3 days per week.

12 Month + contract (or contract to hire, if desired)

You will be responsible for building and evolving the firm's next-generation data platform. The team is focused on designing and implementing scalable data pipelines that move data from operational systems into a centralized data platform built on Kafka, Iceberg, AWS, and Snowflake technologies.

This is a hands‑on engineering role working on large‑scale data ingestion, streaming architectures, data quality, and platform capabilities that support teams across the organization.

What You'll Do

• Design, build, and enhance scalable data pipelines supporting high-volume data ingestion and processing.
• Develop and maintain streaming and batch data solutions using modern data platform technologies.
• Build integrations that move data from source systems such as SQL Server, PostgreSQL, DynamoDB, and other operational platforms into Acadian's data ecosystem.
• Contribute to the evolution of Acadian's Kafka, Iceberg, and cloud‑native data platform initiatives.
• Partner with platform engineering, infrastructure, security, and application teams to deliver reliable and scalable solutions.
• Improve data quality, observability, monitoring, and operational reliability across the platform.
• Leverage AI-assisted development tools as part of the engineering workflow while maintaining strong engineering judgment and code quality standards.
• Work with large‑scale datasets, including multi‑terabyte tables and high‑volume event streams.

Required Qualifications

• 5+ years of experience in Data Engineering, Platform Engineering, or Data Infrastructure Engineering.
• Hands‑on experience building and supporting production data pipelines.
• Experience working with large‑scale data platforms and high‑volume datasets.
• Experience with Kafka or similar event‑streaming technologies.
• Experience with cloud‑based data platforms in AWS.
• Experience working with modern data storage technologies such as Iceberg, Snowflake, Databricks, or similar platforms.
• Experience developing in Python or another modern programming language.
• Ability to explain technical decisions, architecture, and implementation details of systems you have personally built.

Preferred Qualifications

• Apache Flink experience.
• Kubernetes or containerized platform experience.
• Snowflake administration or engineering experience.
• CDC, event streaming, or real‑time data processing experience.
• Financial services experience (nice to have, not required).

What Success Looks Like

• Quickly contributes to the team's Kafka and Iceberg initiatives.
• Builds and enhances production‑grade data pipelines.
• Demonstrates ownership of engineering solutions from design through deployment.
• Operates effectively in a small, highly collaborative engineering team.
• Can clearly articulate system design decisions and implementation tradeoffs.