Senior Data Engineer

DeWinter Group; Boston (MA); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work model

In-office 3 days/week

Competitive compensation

Job summary

DeWinter Financial Services Partner in Boston is looking for a hands-on Data Platform Engineer to design and evolve a next-generation data platform. You will work on scalable data pipelines, streaming architectures, and data quality initiatives across the organization.

You will collaborate with platform engineering, infrastructure, security, and application teams to deliver reliable data solutions, with a focus on Kafka, Iceberg, and cloud-native technologies.

Qualifications

• 5+ years of experience in data or platform engineering.
• Hands-on with production data pipelines.
• Experience with large-scale datasets and streaming.
• Ability to explain decisions and architectures.

Responsibilities

• Design, build, and evolve scalable data pipelines for high-volume ingestion.
• Develop streaming and batch data solutions on modern platforms.
• Integrate data from SQL Server, PostgreSQL, DynamoDB and other systems.
• Advance Kafka, Iceberg, and cloud-native data platform initiatives.
• Collaborate with platform engineering, security, and application teams.
• Improve data quality, observability, and reliability across the stack.
• Leverage AI-assisted development tools while maintaining code quality.
• Handle multi-terabyte tables and high-volume streams.

Skills

Data engineering

Python

Communication

Problem solving

Tools

Kafka

Iceberg

AWS

Snowflake

Databricks

Kubernetes

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.