Data Engineer (Azure)

The CHI Software team is not standing still. We love our job and give it one hundred percent of us! Every new project is a challenge that we face successfully. The only thing that can stop us is… Wait, it’s nothing! The number of projects is growing, and with them, our team too. And now we need a Data Engineer (Azure cloud preferred).

Role Summary:

We’re hiring a Database Engineer to design, build, and operate reliable data platforms and pipelines. You’ll focus on robust ETL/ELT workflows, scalable big data processing, and cloud-first architectures (Azure preferred) that power analytics and applications.

Requirements:

ETL or ELT experience required (ADF/Databricks/dbt/Airflow or similar);
Big data experience required;
Cloud experience required; Azure preferred (Synapse, Data Factory, Databricks, Azure Storage, Event Hubs, etc.);
Strong SQL and performance tuning expertise; hands-on with at least one warehouse/lakehouse (Synapse/Snowflake/BigQuery/Redshift or similar);
Solid data modeling fundamentals (star/snowflake schemas, normalization/denormalization, CDC, etc.);
Experience with CI/CD, Git, and infrastructure automation basics.

Will be a plus:

Streaming pipelines (Kafka, Event Hubs, Kinesis, Pub/Sub) and exactly-once/at-least-once patterns.;
Orchestration and workflow tools (Airflow, Prefect, Azure Data Factory);
Python for data engineering;
Data governance, lineage, and security best practices;
Infrastructure as Code (Terraform) for data platform provisioning.

Responsibilities:

Design, build, and maintain ETL/ELT pipelines and data workflows (e.g., Azure Data Factory, Databricks, Spark, ClickHouse, Airflow, etc.);
Develop and optimize data models, data warehouse/lake/lakehouse schema (partitioning, indexing, clustering, cost/performance tuning, etc.);
Build scalable batch and streaming processing jobs (Spark/Databricks, Delta Lake; Kafka/Event Hubs a plus);
Ensure data quality, reliability, and observability (tests, monitoring, alerting, SLAs);
Implement CI/CD and version control for data assets and pipelines;
Secure data and environments (IAM/Entra ID, Key Vault, strong tenancy guarantees, encryption, least privilege);
Collaborate with application, analytics, and platform teams to deliver trustworthy, consumable datasets.