Data Engineering Manager

Stack

Responsibilities

• ▹Lead a data engineering consulting team of 5-6 – either taking over an existing team or building a new one from scratch
• ▹Oversee project delivery for EMEA customers across data engineering, ML/MLOps and cloud migration projects
• ▹Manage and mentor the team, splitting your time between team management and hands-on project delivery
• ▹Build and optimize scalable, high-performance data pipelines across enterprise customer environments
• ▹Design, implement and maintain ETL/ELT workflows, ingesting structured, semi-structured and unstructured data from systems like Oracle, Snowflake and Redshift
• ▹Big data processing for both batch and streaming workloads, using modern data storage formats such as Delta Lake, Iceberg and Hudi
• ▹Collaborate closely with the cloud, infrastructure and data governance teams to ensure data reliability, observability and monitoring
• ▹Build enterprise data platforms, enable real-time and batch analytics workloads, and migrate legacy systems to modern cloud environments

Requirements

• ▹Consultant mindset and passion for customer experience – advising rather than purely executing on best-in-class technical solutions
• ▹Solid software engineering foundations: a stack-agnostic engineer with a strong understanding of distributed systems and best-in-class engineering principles
• ▹Data and cloud as part of your engineering journey: a role in teams that designed and implemented data-intensive applications, with strong hands-on SQL and NoSQL experience
• ▹Thrive on solving problems and taking ownership, with a natural predisposition to identify and fill organizational gaps (processes, documentation, customer communication, resource allocation)
• ▹Motivated to lead and grow people, whether through formal team management or informal leadership such as leading a project team or owning delivery
• ▹Excitement about mentoring people
• ▹Keen to build strong teams and culture
• ▹Excellent communication skills: adjusting your style, articulating technical concepts to all audiences, and ensuring transparent communication across all levels

Nice to have

• ▹Exposure to real-time data processing systems, Apache Spark, and/or the big data ecosystem

Soft skills

What we offer

• ▹World-class educational benefits: access to Databricks' public and internal courses on distributed data processing, MLOps, Apache Spark, Databricks and cloud migration
• ▹Paid data and cloud certifications, dedicated learning time during work hours, and an internal library
• ▹Flexible working hours and PTO days; how many days you spend in the office is up to you (as a manager, regular face-to-face time with the team is expected)
• ▹4 weeks of paternity leave
• ▹Private medical insurance via Generali
• ▹Employee Assistance Program: access to psychology, financial and legal consulting by phone or in person
• ▹Top-notch working equipment: MacBook Pro, Herman Miller office chair, standing desks
• ▹The chance to shape the company culture in a high-transparency, no-politics environment
• ▹A cozy office in the heart of Budapest (close to the Opera), well-stocked with snacks, drinks and coffee
• ▹An annual team building, movie and wine-tasting nights, and various community events

About the company

DATAPAO is a leading Data Engineering and AI consulting company backed by Databricks, recognized for its delivery standards and rapid growth. It was named Databricks EMEA Emerging Business Partner of the Year (2024), achieved Golden Tier status in the Databricks Partner Program, and made three consecutive appearances on the Financial Times FT1000 list. The data engineering and AI team will grow from ~40 to ~65 over the next 12-18 months. DATAPAO covers the entire data transformation from architecture to implementation and also provides data and AI training and enablement. Backed by Databricks – the creators of Apache Spark – it acts as a delivery partner and training provider for them in Europe, and is also a Microsoft Gold Partner for cloud migration and data architecture on Azure. The selection process has three stages: a screening interview with a technical recruiter (~60 minutes), a technical interview with two senior data engineers (~90 minutes), and a cultural and leadership block run by the Head of People and Head of Consulting (~120 minutes).