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Work setup: Unconfirmed
Employment: Unconfirmed

What you’ll work on

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.
Implement data-governance controls (GDPR-compliant PII handling, lineage, quality tests) using Unity Catalog, Purview, or Fabric’s governance layer.

From the employer’s posting

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.

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.

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.

Tools in this posting

Python
AWS
Azure
Databricks
dbt
Google Cloud (GCP)
Snowflake
Terraform
Airflow
SQL
Spark
Kafka
Grafana
Delta
PySpark

Source

- 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.

- 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).

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:

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).

- 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.

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.

- 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.

Nice to have: - Certifications in any major cloud or Databricks/Snowflake platforms. - Experience designing data pipelines that power GenAI solutions.

- 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:

Job description

View original posting ↗

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:

Certifications in any major cloud or Databricks/Snowflake platforms.
Experience designing data pipelines that power GenAI solutions.

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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