Data Engineer

About Acuative:

Acuative is a global technology services provider delivering advanced network, IT, and digital transformation solutions to enterprise, service provider, and public-sector clients. With a strong regional presence and a track record of delivering large-scale, mission-critical engagements, Acuative combines deep technical expertise with a service-first culture. We are expanding our data and analytics practice and are looking for talented professionals to join a strategic data platform and business intelligence engagement.

Role Summary:

Acuative is seeking a skilled Data Engineer to build and operate the data pipelines at the heart of an enterprise Lakehouse and business intelligence engagement. You will develop automated ingestion pipelines and ETL/ELT frameworks, optimize data transformations, and ensure that high-quality, reliable data flows through every layer of the platform.

Key Responsibilities:

• Develop and maintain automated data ingestion pipelines from a variety of source systems.
• Build and extend reusable ETL/ELT frameworks aligned with the platform architecture.
• Develop and optimize 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 tools such as Azure Data Factory or Apache Airflow.
• Collaborate with the architecture and BI teams to deliver curated, analytics-ready datasets.
• Document pipelines, frameworks, and operational procedures.

Required Qualifications:

• 5+ years of hands-on experience in data engineering.
• Proficiency in Python, SQL, and Apache Spark.
• Experience with cloud data tools and modern data platform ecosystems.
• Practical experience with pipeline orchestration tools (e.g., Azure Data Factory, Apache Airflow).
• Solid understanding of data quality management and ETL/ELT best practices.

Nice to Have:

• Experience with Lakehouse platforms such as Databricks, Synapse, or Snowflake.
• Familiarity with CI/CD and DevOps practices for data pipelines.
• Relevant cloud or data engineering certifications.