Portfolio Data Engineering Manager (Poland)

Addepar; Town of Poland (NY); On-site; USD 101,581 - 121,897; Full time

Job summary

Addepar is seeking an experienced Data Engineering Manager to lead Portfolio Data Engineering in Poland. You will manage an engineering team, drive delivery of complex data ingestion and processing pipelines, and collaborate with product peers in an agile environment.

The role focuses on automating workflows and scaling data operations to support clients. You will work within the Addepar Platform to improve data quality, throughput, and reliability, leveraging PySpark and Databricks Lakehouse

Qualifications

• Experience leading data engineering teams.
• Strong problem-solving and decision-making skills.
• Experience delivering data pipelines and platform-scale data solutions.

Responsibilities

• Serve as a player/coach to lead Portfolio Data Engineers in Poland.
• Drive technical direction for the portfolio data engineering team across regions.
• Build and manage a team of engineers.
• Partner with cross-functional teams to design scalable data solutions that meet client needs.
• Lead pipelines for ingestion, analysis, and enrichment of financial data.
• Advocate for automation to increase throughput and data quality.
• Develop process controls and metrics to improve data quality and efficiency.
• Deliver high-quality results in a fast-paced environment.

Skills

Data engineering leadership

Problem solving

Experience as individual contributor

Education

Bachelor's degree in Computer Science

Tools

PySpark

Python

Java

SQL

Relational Databases

Job description

Addepar is seeking an experienced Data Engineering Manager to lead Portfolio Data Engineering in Poland. You will manage an engineering team, drive delivery of complex data ingestion and processing pipelines, and collaborate with product peers in an agile environment.

The role focuses on automating workflows and scaling data operations to support clients. You will work within the Addepar Platform to improve data quality, throughput, and reliability, leveraging PySpark and Databricks Lakehouse