Data Engineer (Hadoop / Spark)

About the Role

We are currently seeking a Database Developer to join us in working on our products.

What You’ll Do

• Development and support of database structures and objects, writing queries and stored procedures (PostgreSQL, MS SQL)
• Optimization of the existing database structure and code
• Modernization and creation of new reports, widgets, and dashboards for Reporting products (a new one and Veeam ONE)
• Migrating and reworking the current business logic to a new, modern architecture in the current product and adapting it for the new product
• Working closely with teams of analysts and QA

Technologies we work with:  

• PostgreSQL, pgTAP or analogs, TFS + Git, MS SQL, T-SQL

What You’ll Bring

• 3+ years of experience with commercial products
• Hands-on experience with PostgreSQL
• Strong knowledge of database theory
• Ability to write complex SQL scripts, functions, and stored procedures
• Comfortable reading, understanding, and maintaining existing code
• Flexibility and willingness to learn
• Ownership mindset: reliable, detail-oriented, disciplined, and able to deliver efficiently

What You’ll Get 

• 26 paid days off annually, plus 4 extra global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares
• Paid parental, maternity, and paternity leave
• Fully covered family medical plan, dental, rehab, and vaccinations
• Life, critical illness, and disability insurance
• Employer pension contribution via PPK
• Monthly Edenred allowance of 450 PLN for meals
• MultiSport card fully covered by Veeam, giving access to sports facilities nationwide
• Up to 12 free therapy sessions annually, plus legal and financial advice
• Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops and learning events like our annual Global Day of Learning

Please note: If the applicant is permanently present outside of Poland, Veeam reserves the right to refuse to consider the application for a job. Remote job is only possible in case the employee is located in Poland.

#LI-VE1

About the Role

You’ll lead two core systems inside Veeam Data Command Center: the Knowledge Graph and the hyperscale data lake integrations. Together, they help customers understand where sensitive data lives, who can access it, how it moves, and whether AI models trained on it can be trusted. 

You’ll lead multiple teams building a searchable, security-aware graph that works at enterprise scale. This role is for a hands-on technical leader who can set clear direction, grow strong teams, and deliver reliable systems—while building an AI-first engineering culture with high standards for quality and security. 

What You’ll Do

• Set the technical vision and end-to-end architecture for the Knowledge Graph, including the data model, storage engine, and query layer at very large scale 

• Guide the evolution of the graph schema for data sources, identities, access, classifications, and lineage (property graph and/or RDF) using Amazon Neptune and/or Neo4j 

• Own the strategy for hyperscale lake and lakehouse integrations, including connectors and scanning engines that ingest metadata and lineage from Delta Lake, Iceberg, Parquet/Avro, and platforms like Azure Data Lake, AWS S3/Glue, and BigQuery without disrupting production 

• Drive performance and reliability, including standards for indexing, partitioning, and query planning, and tuning traversals and queries (Gremlin, Cypher/openCypher, SPARQL) 

• Build and scale an AI-first engineering approach where teams use tools like Claude Code, Cursor, and Copilot responsibly, with guardrails for security, maintainability, and code quality 

• Invest in reusable engineering building blocks (including “Claude skills” and agent workflows) that make teams faster and more consistent 

• Own delivery outcomes: roadmap execution, operational readiness, incident learning, and cross-team alignment 
• Hire, coach, and develop leaders, including engineering managers and senior/staff engineers, with clear expectations and growth paths 

What You’ll Bring

• 10+ years of software engineering experience in data infrastructure, graph systems, or distributed data platforms 

• 4+ years of engineering leadership experience, including leading through managers and scaling multiple teams 

• Strong production experience with Amazon Neptune and/or Neo4j, including scaling, operations, and trade-offs (property graph vs. RDF) 

• Proven ability to lead graph modeling for complex domains, including lineage and permissions at enterprise scale 

• Deep knowledge of Gremlin, Cypher/openCypher, and/or SPARQL, including performance tuning and query design best practices 

• Experience with data lakes/lakehouses (Delta Lake, Iceberg, Parquet) across major cloud platforms (Azure Data Lake, AWS S3/Glue, BigQuery) 

• Experience designing and operating distributed systems using tools like Spark, Flink, or Presto/Trino, with strong judgement on scalability and cost 

• Strong backend background in Go and/or Python, with the ability to review designs, guide decisions, and unblock teams 

• Practical experience using AI-assisted development tools and the ability to set standards that keep AI-assisted code secure and high quality 

Bonus Skills

• Experience operating graph systems at massive scale 

• Background in data security, access governance, and policy controls 

• Experience with AI/ML governance tools and practices (e.g., MLflow, Databricks Mosaic AI) 

• Experience building custom agents, MCP-based workflows, or reusable engineering automation 

• Infrastructure-as-Code experience (e.g., Terraform or Pulumi) 

• Contributions to graph standards or communities (GQL, openCypher, SPARQL) 

About the Role

We are seeking exceptional Senior Engineer- Hyperscale Analytics to design and scale next-generation data processing and analytics platforms that power OLTP, OLAP, and large-scale distributed data systems. You will build and optimize pipelines and services that handle billions of records daily, enabling real-time transactions, analytical insights, and AI-driven decisioning.

What You’ll Do

• Transactional & Analytical Systems: Design and implement highly scalable OLTP systems for real-time workloads and OLAP systems for complex analytical queries on massive datasets
• Distributed Processing: Build, optimize, and maintain large-scale batch and streaming pipelines using frameworks such as Apache Spark, Flink, Presto/Trino, or Kafka Streams
• System Performance & Scale: Optimize systems for low-latency queries, high-throughput ingestion, and interactive analytics, ensuring seamless performance as data volumes scale to petabytes
• Data Infrastructure: Develop and integrate with modern storage and processing systems (e.g., Snowflake, BigQuery, Redshift, Cassandra, HDFS, Delta Lake, Iceberg) to support hybrid analytical/transactional workloads
• Reliability & Observability: Ensure high availability, reliability, and monitoring across large compute and storage clusters with automated failover and recovery
• Collaboration: Partner with data scientists, ML engineers, and product teams to build unified, secure, and cost-efficient data platforms

What You’ll Bring

• 6+ years of professional software engineering experience, with a significant portion focused on data infrastructure, distributed systems, or large-scale analytics platforms
• Deep experience with distributed data processing frameworks (e.g., Spark, Flink, or similar)
• Strong background in cloud-scale data architecture (AWS, Azure, or GCP) — data lakes, warehouses, streaming platforms
• Proficiency in one or more of: Python, Java, Scala, or Go
• Experience with modern data warehouse/lakehouse technologies (e.g., Snowflake, Databricks, BigQuery, Redshift)
• Solid understanding of data modeling, ETL/ELT design, and pipeline orchestration (e.g., Airflow, dbt)
• Track record of designing for scale, reliability, and cost efficiency in production systems

Bonus Skills

• Experience with HTAP (Hybrid Transactional/Analytical Processing) systems or real-time analytics platforms
• Familiarity with data lakehouse architectures and formats like Parquet, ORC, Delta, Iceberg, Hudi
• Knowledge of containerized deployments (Docker, Kubernetes) and cloud-native data architectures (AWS Redshift, GCP BigQuery, Azure Synapse)
• Background in query engine development or contributing to open-source OLAP/OLTP frameworks