Position Summary
As a Senior Data Engineer, you will play a pivotal role in driving data
solutions that align with business objectives. Your responsibilities will
include collaborating with stakeholders to analyze business requirements and
translate them into technical specifications. You will design and implement ETL
processes to ensure accurate data transformation and loading into data
warehouses, maintaining data integrity and quality. You will also build and
maintain robust data pipelines for efficient data ingestion and processing from
various sources, optimizing data flow for scalability and timely availability
for analytics.
Requirements
Minimal Qualifications:
Bachelor’s degree in computer science, Information Technology, or a related
field.
3+ experience in big data development and overall 5+ experience in data
related work
Proficiency in data warehousing concepts, architectures, and methodologies and
Experience with ETL processes to ensure accurate and timely data integration
and processing.
Knowledge of business operations and objectives to identify key data
requirements and ensure that data products align with strategic goals
Understanding of the data product lifecycle, ensuring alignment with business
needs, and meeting user requirements
Expert in ADF, Databricks, Azure function, Python, API Development
Ability to work collaboratively in a team environment.
Excellent verbal and written communication skills in English
Preferred Qualifications:
Certification in data management or data modeling (e.g., CDMP, DAMA) is a plus.
Preferred knowledge in the set-up and administration of Hadoop, Spark
platform, Azure Datalake, Databricks and other related architecture components.

Job Description
Main Tasks
Business Requirement Analysis: Collaborate with stakeholders to analyze
business needs and translate them into technical requirements. Understand the
objectives of various projects to ensure that data solu-tions align with
business goals.
ETL Development: Design and implement ETL processes based on data models.
Ensure data is accurately transformed and loaded into data warehouses or other
storage solutions, maintaining data integrity and quality.
Data Pipeline Development: Build and maintain robust data pipelines that
facilitate the smooth inges-tion and processing of data from various sources.
Optimize data flow for efficiency and scalability, ensur-ing timely
availability of data for analytics and reporting.
Data Modeling and Warehousing: Contribute to data modeling efforts by
designing logical and physical data models that support business requirements.
Collaborate with the team to enhance data warehousing solutions for improved
data accessibility and performance.
Data Service Provision: Provide data services to internal teams and external
clients, ensuring that data is readily available and usable for various
applications and analyses. Support users in accessing and utilizing data
effectively.