Senior Data Engineer
In short
Senior Data Engineer role at a global SaaS company focusing on customer engagement. Responsibilities include designing data pipelines, data modeling, IaC with Terraform, AI/ML enablement, and MLOps. Requires 5+ years of experience in data systems, Python, advanced SQL, AWS, Snowflake, Spark, and Docker/Kubernetes.
AI-written summary based on the listing content.
About the Opportunity & Our Client
We are a specialized tech recruitment agency partnering with a premier, global SaaS product company that powers intelligent customer engagement and data-driven marketing automation for hundreds of thousands of businesses worldwide.
Processing massive volumes of events daily, their distributed platform transforms real-time data into predictive, automated customer experiences. The company is known for its engineering-first culture, mature cloud-native infrastructure, and strong commitment to work-life balance and continuous learning.
We are seeking an experienced Senior Data Engineer to join the core Data Platform team. In this role, you will design robust, high-throughput architectures, scale modern data stacks, and collaborate closely with AI/ML teams to bring production-grade intelligence into live environments.
Role Overview
As a Senior Data Engineer, you will own the end-to-end lifecycle of core data systems. You will balance immediate high-performance querying needs with long-term schema scalability, write clean and testable Python code, deploy your own cloud infrastructure via Terraform, and bridge the gap between business reporting, platform telemetry, and cutting-edge MLOps pipelines.
Key Responsibilities
- Pipeline Architecture & Engineering: Design, develop, and operate resilient, high-throughput data processing pipelines transforming data across distributed ecosystems.
- Data Modeling & Storage Strategy: Lead schema evolution, DDL/DML design, and partitioning strategies across relational and analytical warehouses to optimize cost, latency, and throughput.
- Engineering Rigor & IaC: Advocate for modern software engineering standards by managing your own cloud infrastructure via Terraform and maintaining automated test suites and robust CI/CD pipelines.
- AI & MLOps Enablement: Partner closely with Data Science and AI specialists to architect feature stores, model ingestion workflows, and production-grade MLOps pipelines.
- Reliability & Observability: Monitor, optimize, and scale containerized data services and scheduled DAGs running in orchestrated environments (Kubernetes, Airflow, Docker).
- Technical Leadership & Collaboration: Serve as a subject-matter expert for cross-functional stakeholders, translating complex architectural trade-offs into practical, business decisions.
Technology Stack
- Languages: Python (primary), Advanced SQL
- Cloud & Infrastructure: AWS (S3, Aurora, RDS), Terraform, Docker, Kubernetes
- Data Warehousing & Lakehouse: Snowflake, Apache Iceberg, Apache Druid
- Data Processing & Transformation: Apache Spark, dbt
- Orchestration & BI: Apache Airflow, Looker
Candidate Profile
Required:
- 5+ years of professional experience in software engineering with a primary focus on designing, scaling, and maintaining production data systems.
- Deep Data Architecture Expertise: Proven track record in data modeling, query optimization, and schema design for both high-concurrency relational databases (MySQL / Aurora / RDS) and cloud analytical warehouses.
- Strong Cloud & IaC Skills: Hands-on experience provisioning and managing infrastructure using Terraform in a major cloud environment (preferably AWS).
- Software Rigor: Practical mindset toward automated testing (unit, integration, regression), CI/CD pipelines, and maintainable, modular Python code.
- Distributed Processing Experience: Real-world delivery experience using modern data tools (e.g., Snowflake, Apache Spark, dbt, Apache Iceberg).
- Collaborative Communication: Strong command of English (B2/C1+) with the ability to articulate architectural decisions clearly to technical peers and non-technical stakeholders alike.
Nice to have:
- Experience building or supporting MLOps workflows and feature stores alongside Data Science teams.
- Hands-on experience with real-time analytics engines (e.g., Apache Druid or ClickHouse).
- Familiarity with containerized orchestration using Kubernetes and Helm.
What’s on Offer?
- Tax Efficiency: 75% KUP (Copyright Tax Deductible Costs / Koszty Uzyskania Przychodu) for creative/software engineering contributions.
- Annual Incentive: 10% annual bonus tied to personal and company performance.
- Flexibility: 100% remote work from anywhere in Poland, supported by a home office stipend.
- Health & Wellness: Comprehensive private medical cover via LuxMed (including dental and vision subsidies) + complimentary mental well-being app subscription.
- Sports & Recreation: MultiSport Plus card.
- Retirement & Long-Term Rewards: Pension scheme matching up to 1.5%, plus a paid 4-week sabbatical with a financial bonus after 5 years of service.
- Growth Budget: Direct access to LinkedIn Learning, conference allowances, and personalized development pathways.
| Published | 2026-10-29 |
| Expires | 2026-12-16 |
| Source |
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