Data Engineer

fulcrumdigital; Dublin; On-site; EUR 80,000 - 120,000; Full time

Job summary

fulcrumdigital is seeking a highly skilled Data Quality Engineer with strong Data Engineering expertise to ensure the accuracy, reliability, and scalability of enterprise data platforms in cloud environments.

You will validate data across large-scale pipelines using Databricks, PySpark, Hadoop, Hive, and SQL, build quality frameworks, and collaborate with data engineers and analysts to ensure reliable data delivery in Lakehouse architectures.

Qualifications

• 3+ years of experience in data engineering or data testing.
• Hands-on experience with Databricks and PySpark.
• Strong experience with Hive, HDFS, Spark and related Apache technologies.
• Advanced SQL for large-scale data validation and analysis.
• Experience with Data Warehouses, Data Lakes and Lakehouse architectures.
• Understanding of Star Schema, Snowflake Schema, and dimensional modeling.
• Cloud platforms experience (Azure, AWS, or GCP).

Responsibilities

• End-to-end validation of data pipelines across ingestion, transformation and consumption layers.
• Source-to-target reconciliation and data quality checks.
• Identify, investigate and resolve data anomalies and inconsistencies.
• Define and implement data quality frameworks, metrics and controls.
• Develop and validate data pipelines using PySpark and Databricks.
• Support ETL/ELT workflows and ensure data integrity throughout the lifecycle.
• Collaborate with Data Engineers, Architects and Analysts.

Skills

Databricks

PySpark

SQL

Data Modeling

Cloud Platforms

Data Warehouses/Lakes/Lakehouse

Star/Snowflake Schema

Dimensional Modeling

Hadoop Ecosystem

Data Quality & Validation

Tools

Databricks

PySpark

Hadoop

Hive

HDFS

Spark

Delta Lake

Unity Catalog

Airflow

Kafka

Job description

Role Overview

We are looking for a highly skilled Data Quality Engineer with strong Data Engineering expertise to ensure the accuracy, reliability, and scalability of enterprise data platforms. The ideal candidate will possess hands‑on experience with Databricks, PySpark, Hadoop, Hive, and Cloud technologies, along with advanced SQL skills to validate data across large‑scale data pipelines and Lakehouse architectures.

Required Experience

3+ years of experience in Data Engineering, Data Quality Engineering, or Data Testing.

Hands‑on experience with Databricks and PySpark.

Strong experience in Hadoop ecosystem components such as Hive, HDFS, Spark, and related Apache technologies.

Advanced SQL expertise for large‑scale data validation and analysis.

Experience working with Data Warehouses, Data Lakes, and Lakehouse architectures.

Understanding of Star Schema, Snowflake Schema, and dimensional modeling.

Experience with cloud platforms (Azure, AWS, or GCP).

Requirements

Data Quality & Validation

• Perform end‑to‑end validation of data pipelines across ingestion, transformation, and consumption layers.
• Execute source‑to‑target reconciliation and data quality checks.
• Identify, investigate, and resolve data anomalies and inconsistencies.
• Define and implement data quality frameworks, metrics, and controls.

Data Engineering & Processing

• Develop and validate data pipelines using PySpark and Databricks.
• Work with large‑scale datasets in Hadoop, Hive, and Lakehouse environments.
• Support ETL/ELT workflows and ensure data integrity throughout the data lifecycle.
• Optimize data processing jobs for performance and scalability.

SQL & Analytics

• Write advanced SQL queries for data profiling, reconciliation, and root cause analysis.
• Perform complex joins, window functions, CTEs, aggregations, and query optimization.
• Validate business rules and transformation logic against source systems.

Lakehouse & Cloud Platforms

• Validate and monitor data across Databricks Lakehouse architecture.
• Work with cloud platforms such as Azure, AWS, or GCP.
• Collaborate with Data Engineers, Architects, and Analysts to ensure reliable data delivery.

Defect & Incident Management

• Analyze production issues and conduct root cause analysis.
• Track and manage data defects through resolution.
• Implement proactive monitoring and automated quality checks.

Required Experience

6+ years of experience in Data Engineering, Data Quality Engineering, or Data Testing.

Hands‑on experience with Databricks and PySpark.

Strong experience in Hadoop ecosystem components such as Hive, HDFS, Spark, and related Apache technologies.

Advanced SQL expertise for large‑scale data validation and analysis.

Experience working with Data Warehouses, Data Lakes, and Lakehouse architectures.

Understanding of Star Schema, Snowflake Schema, and dimensional modeling.

Experience with cloud platforms (Azure, AWS, or GCP).

Preferred Skills

• Automated data testing frameworks.
• Data observability and monitoring tools.
• CI/CD implementation for data pipelines.
• Experience with Delta Lake, Unity Catalog, or similar technologies.
• Knowledge of Airflow, Kafka, or other Apache ecosystem tools.