Data Engineer - ETL / Data Warehousing

Modern, we're looking for a driven and detail-oriented Data Engineer with a strong consulting background to join our Client Technology Team. In this role, you will serve as a subject matter expert (SME) for our Enterprise Data Product Platform, DataOS, and play a critical part in designing and implementing scalable, high-performance data solutions. You'll bridge the gap between business requirements and technical execution, ensuring that data systems are robust, efficient, and aligned with organizational Collaborate with business stakeholders to deeply understand data needs and translate them into conceptual, logical, and physical data models.

• Partner with data stewards, analysts, and other technical teams to define and deliver data product roadmaps.
• Architect, design, and develop scalable, distributed data processing solutions leveraging DataOS.
• Enhance and optimize Spark-based workflows for efficiency, scalability, and Evaluate and apply the most suitable technologies (e. g., cloud platforms, Hadoop, NoSQL, and traditional data warehousing to meet business challenges.
• Contribute to solution design discussions and provide strategic insights during planning and problem-solving sessions.
• Work in tandem with Business Analysts and Solution Architects to create data models that fulfill business objectives while maintaining alignment with Enterprise Architecture.
• Coordinate with Data Architects and Program Managers, participating in project meetings and progress Bachelor's degree in Computer Science, Business Analytics, or a related field.
• Minimum 5 years of experience in data engineering, data architecture, or analytics.
• Proficiency in Spark, Python, SQL, and API integration frameworks.
• Strong understanding of modern data architectureincluding cloud-based data lakes, data warehouses, and data marts.
• Experience in dimensional modeling, star schemas, ETL pipelines, and data streaming (Kafka).
• Proven ability to ensure data quality, integrity, and reliability using validation and monitoring tools.
• Hands-on experience with workflow orchestration tools such as DBT and Airflow (or similar).
• Practical knowledge of both relational (PostgreSQL, MySQL) and NoSQL (Redshift, BigQuery, Cassandra) databases.
• Familiarity with Docker, Kubernetes, and cloud storage technologies (Azure Data Lake, AWS S3 Google Cloud Storage).

(ref : hirist.tech)