Data Engineer - ETL/Data Warehousing

Description:

At 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 goals.

Responsibilities:

- 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 cost-effectiveness.

- 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 discussions.

Requirements:

- 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).