Data Engineer (AI), contract

Responsibilities

• Develop and manage end-to-end data pipelines, covering data ingestion, transformation, quality assurance, and integration to support enterprise analytics solutions.

• Partner with solution design and business stakeholders to define data needs and compile complex datasets aligned with business objectives.

• Design, implement, and optimize analytics solutions to meet both technical and functional requirements.

• Collaborate with data architects to maintain consistency and integrity of data models.

• Build and maintain scalable infrastructure for efficient data extraction, transformation, and loading (ETL) across diverse data sources.

• Develop tools and frameworks to enable data analysts and data scientists to efficiently build and enhance data models.

• Work closely with DevOps teams to ensure stable and reliable deployment and operation of data platforms.

• Collaborate with data analysts and scientists to design and develop APIs supporting analytics and AI use cases.

Requirements

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related discipline.

• Minimum 3 years of experience in SQL/PostgreSQL, data engineering, and BI solutions, including integration with third-party systems.

• Hands-on experience with technologies such as ERP systems, Spark, Scala, Python, SQL scripting, relational databases (e.g., data warehouses), NoSQL platforms (e.g., MongoDB, Cassandra), and cloud platforms (e.g., Azure).

• Proven experience with modern data platforms and tools such as Data Lake, Databricks, Data Factory, and BI dashboard development.

• Strong track record in handling large-scale, complex datasets and building end-to-end pipelines on both on-premise and cloud environments.

• Proficiency in coding for data management, data warehousing, and unstructured data processing.

• Experience in energy or other asset-heavy industries is advantageous.

• Familiarity with developer productivity tools such as GitHub Copilot.

• Understanding of Generative AI concepts (e.g., RAG), orchestration frameworks (e.g., LangChain, LlamaIndex), and vector databases.