Data Engineer – Enterprise Lakehouse & BI

About the role

Acuative is expanding its data and analytics practice and seeks a skilled Data Engineer to design, build, and operate data pipelines for an enterprise Lakehouse and business intelligence engagement.

Key responsibilities

• Develop and maintain automated data ingestion pipelines from diverse source systems.
• Build and extend reusable ETL/ELT frameworks aligned with platform architecture.
• Write and optimise data transformation scripts and jobs for performance and cost efficiency.
• Implement data quality checks, validation rules, and monitoring across pipelines.
• Orchestrate and schedule pipelines using Azure Data Factory, Apache Airflow, or similar tools.
• Collaborate with architecture and BI teams to deliver curated, analytics‑ready datasets.
• Document pipelines, frameworks, and operational procedures.

Required profile

• 5+ years of hands‑on data engineering experience.
• Proficiency in Python, SQL, and Apache Spark.
• Experience with cloud data tools and modern data platform ecosystems.
• Practical experience with pipeline orchestration tools such as Azure Data Factory or Apache Airflow.
• Strong understanding of data quality management and ETL/ELT best practices.

Required skills

• Python
• SQL
• Apache Spark
• Azure Data Factory
• Apache Airflow
• Databricks (Lakehouse platform)
• Synapse (Lakehouse platform)
• Snowflake (Lakehouse platform)
• CI/CD for data pipelines
• DevOps practices for data pipelines