We’re expanding our Data Engineering practice and looking for a senior-level engineer who can design, automate, and scale modern lakehouse and warehouse platforms on Azure, AWS, or GCP. In this role, you’ll not only partner with product managers, analysts, and data scientists to unlock governed, high-quality data for batch and streaming use-cases, but also lead internal initiatives for driving proof-of-concepts, involved in pre-sales activities, building reusable frameworks, and setting reference architectures that raise the bar for the entire team. You’ll steward our engineering standards and turn innovative ideas into production-ready solutions that accelerate both client projects and our own platform evolution.
What you’ll do:
- Drive end-to-end data‐platform projects, from requirement workshops to production hand-over, using Databricks, Microsoft Fabric, Snowflake, and associated cloud services.
- Build and optimize ETL/ELT pipelines (batch, micro-batch, and true real-time) with Python/PySpark, dbt, Airflow, Kafka/Flink, and cloud-native tools.
- Automate deployment of data stacks via Git-based CI/CD, observability, and cost-controls at every stage.
- Implement data-governance controls (GDPR-compliant PII handling, lineage, quality tests) using Unity Catalog, Purview, or Fabric’s governance layer.
- Mentor mid-level engineers, codify best practices, and contribute to architecture standards and run books.
- Troubleshoot production workloads, continually improve performance, document models, dictionaries, and run books for stakeholders.
What we’re looking for:
- 5+ years in data engineering, with at least 3 years in a cloud environment (AWS, Azure, or GCP).
- Expert knowledge of data-warehouse modeling concepts (star/snowflake, Data Vault, lakehouse).
- Hands-on experience with Databricks (Spark/Delta Live Tables), Microsoft Fabric (OneLake, Data Pipelines), or Snowflake, including performance tuning and cost optimization.
- Proven track record building both batch and streaming pipelines.
- Strong Python and SQL.
- Solid grasp of CI/CD (GitHub Actions, Azure DevOps, or similar) and Infrastructure-as-Code (Terraform or CloudFormation).
- Familiarity with DataOps, observability (Great Expectations, Monte Carlo, Prometheus/Grafana), and security best practices.
Nice to have:
- Experience designing data pipelines that power GenAI solutions
- Certifications in any major cloud or Databricks/Snowflake platforms.
We appreciate the interest of all applicants. Please note that only those whose qualifications align closely with the position requirements will be contacted for the next steps in the selection process.
All applications will be handled with confidentiality.
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Skills Required
- 5+ years of data engineering experience
- At least 3 years of experience in a cloud environment such as AWS, Azure, or GCP
- Expert knowledge of data warehouse modeling concepts, including star schema, snowflake schema, Data Vault, and lakehouse architectures
- Hands-on experience with Databricks, Microsoft Fabric, or Snowflake, including performance tuning and cost optimization
- Experience building both batch and streaming data pipelines
- Strong Python and SQL skills
- Experience with CI/CD tools such as GitHub Actions or Azure DevOps
- Experience with Infrastructure-as-Code tools such as Terraform or CloudFormation
- Familiarity with DataOps, observability, and security best practices
- Experience with data quality and observability tools such as Great Expectations, Monte Carlo, Prometheus, or Grafana
- Experience designing data pipelines that power generative AI solutions
- Certifications in a major cloud, Databricks, or Snowflake platform
What We Do
IWConnect is a global, multi-disciplinary enterprise technology consultancy headquartered in Bitola, North Macedonia, with offices across four countries. Founded in 2004 (formerly InterWorks), its 300+ people deliver enterprise AI, data integration, application engineering, cloud, and managed services, helping enterprises connect fragmented systems, move technology from pilot to production, and operate reliably at scale.
