Senior Data Engineer

fulcrumdigital; Dublin; On-site; EUR 90,000 - 120,000; Full time

Job summary

Fulcrum Digital is seeking a highly skilled Data Quality Engineer with strong Data Engineering expertise to ensure accuracy, reliability, and scalability of enterprise data platforms. The ideal candidate will work with Databricks, PySpark, Hadoop, Hive, and cloud technologies to validate data across large-scale pipelines and Lakehouse architectures.

You will lead end-to-end data quality validation, implement quality frameworks, and optimize data processing jobs while collaborating with Data

Qualifications

• 6+ years in Data Engineering, Data Quality Engineering, or Data Testing.
• Hands-on Databricks and PySpark experience.
• Strong knowledge of Hadoop ecosystem (Hive, HDFS, Spark) and Apache tech.
• Experience with data warehouses, lakes, and lakehouse architectures.
• Understanding of Star Schema, Snowflake Schema, and dimensional modeling.
• Experience with cloud platforms (Azure, AWS, or GCP).

Responsibilities

• Data Quality & Validation: 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.
• Develop and validate data pipelines with PySpark and Databricks.
• Monitor and optimize data processing in Lakehouse environments.

Skills

Advanced SQL

Data Validation

Data Quality

Tools

Databricks

PySpark

Hadoop

Hive

Azure

AWS

GCP

Job description

Who are we

Fulcrum Digital is an agile and next-generation digital accelerating company providing digital transformation and technology services right from ideation to implementation. These services have applicability across a variety of industries, including banking & financial services, insurance, retail, higher education, food, healthcare, and manufacturing.

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

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).

Key Responsibilities

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.