Description

Design and implement scalable data systems and pipelines to transform core data assets across SQL, NoSQL, and cloud-based platforms.

Responsibilities

• Build and maintain data systems, pipelines, and collection systems to optimize statistical efficiency.
• Define high-level migration plans to bridge gaps between current and future state architectures.
• Lead technical analysis to identify deficiencies in the environment and recommend architectural improvements.
• Acquire data from primary and secondary sources while managing data catalogs to promote asset reuse.
• Present cost/benefit analyses to leadership to drive architectural decision-making.

Required Skills

• 5+ years of experience in data engineering or related roles.
• Expertise in Python, Spark, and SQL.
• Experience building performance-driven solutions on Microsoft Azure using Azure Data Factory, Azure Synapse, Databricks, and Azure Data Lake.
• Proficiency with Cosmos DB and both SQL and NoSQL applications on Big Data platforms.
• Hands-on experience with SSAS Tabular models, Power BI, Dataflows, and DAX.
• Knowledge of streaming technologies such as Kafka, IBM MQ, or EventHub.
• Strong background in SQL database design and data modeling.
• Understanding of network and data security architecture.
• Ability to work with elastic architectures and diverse technical disciplines including data warehousing, APIs, or data integration.

Preferred Skills

• Experience with operational data science, machine learning, or AI solutions.
• Experience with Google Cloud Platform.
• Data engineering certification, such as IBM Certified Data Engineer.

Key Skills

Education

Any Graduate