Senior Data Engineer

As a Data Engineer at GoodHabitz, you’ll be part of an exciting journey as we continue our migration to AWS, enhancing and modernizing our data infrastructure to support a rapidly growing business. In our scale-up environment, adaptability and problem-solving are key. This role is instrumental in designing, building, and optimizing scalable, cloud-native data architectures that meet evolving business and analytical needs.

You’ll collaborate with cross-functional teams to implement robust, efficient data pipelines and help shape a high-performing data platform centered around Databricks on AWS, Unity Catalog, Delta Live Tables, and other related services. If you're eager to make a real impact and contribute to a forward-thinking data engineering culture, we’d love to hear from you.

Key Responsibilities:

· Data Architecture & Design: Design scalable, high-performance data architectures leveraging AWS native services, Databricks (Unity Catalog, Delta Live Tables, Delta Lake), and modern best practices.

· Pipeline Development: Build and maintain efficient, production-grade ETL/ELT pipelines using Databricks (PySpark, SQL), with a strong focus on data quality, lineage, and maintainability.

· Cloud Infrastructure: Combine open-source technologies with AWS and Databricks services to create optimized and automated data workflows.

· Data Governance & Quality: Implement best practices for data governance, including lineage tracking, quality checks, and access control using Unity Catalog and related tools.

· Automation & CI/CD: Implement deployment automation and pipeline monitoring using Terraform for infrastructure-as-code and GitLab for version control and CI/CD workflows.

· Performance Optimization: Optimize data processing performance using techniques such as data partitioning, indexing, caching, and efficient storage formats.

· Collaboration & Mentoring: Work closely with data analysts, software engineers, and other stakeholders to gather requirements and deliver solutions. Mentor junior team members and promote engineering best practices.

Requirements

• 5+ years of experience in data engineering, including strong exposure to cloud-native architectures and modern data platforms.
• Deep understanding of ETL processes, data modeling, data warehousing, and Medallion architecture.
• Hands-on experience with Databricks on AWS, including Unity Catalog, Delta Live Tables, and related tools.
• Proficiency in Python, PySpark, and SQL for large-scale data processing and transformation.
• Experience with infrastructure-as-code tools, especially Terraform (bonus if you’ve worked with AWS-native provisioning and configuration tools).
• Experience working with GitLab for version control, repository management, and CI/CD workflows.
• Strong knowledge of SQL performance tuning, query optimization, and handling large datasets efficiently.
• Comfortable working with orchestration tools such as Apache Airflow, AWS Step Functions, or similar.

Nice to Have:

• Experience with containerization and serverless frameworks (e.g., Docker, Kubernetes, AWS Lambda).
• Familiarity with monitoring/observability tools such as Prometheus, Grafana, AWS CloudWatch, or Databricks-native logging/monitoring.
• Exposure to data security best practices and compliance in cloud environments.

Benefits

Here's a glimpse of what's waiting for you:   

• A competitive salary package that rewards your hard work.  
• 25 paid vacation days. And if that's not enough, you can purchase up to 10 more.  
• A world of growth and development opportunities to enhance your skills. You'll have unlimited access to our treasure trove of GoodHabitz resources and MyAcademy .  
• Access to mental coaching through our partner, OpenUp, to keep your mind in top shape.  
• An annual do-good-day, fully paid, so you can contribute to a cause you're passionate about.   
• Travel and expense reimbursement because we've got your journey covered.   
• Pension and disability insurance, securing your financial well-being in the long run.   
• A hybrid way of working .   
• Working in a company that welcomes artificial intelligence and uses it to improve internal processes and push AI-powered features quickly .  
• A company laptop.