Senior Data Engineer

About the Company

We are seeking a skilled Senior Data Engineer with expertise in cloud-native data pipeline development within Microsoft environments. The ideal candidate will have a strong background in data architecture, data modelling, and the ability to implement high-performance data solutions that support our business objectives. You will play a key role in driving data-driven decision-making and enabling scalable analytics solutions across the organization.

About the Role

Required Qualifications to be successful for the senior role:

Responsibilities

• Strong experience in end-to-end data architecture and design, including ingestion, modeling, storage, and orchestration (ex. Medallion).
• Proficiency in cloud-native engineering, with a focus on Microsoft technologies (Fabric, Azure Data Factory, Synapse) but open to other cloud platforms (AWS, Google Cloud) with comparable tools.
• Hands-on experience with data warehousing and data lake solutions (e.g., BigQuery, Azure Data Lake, Delta Lake, Amazon Redshift).
• Proficiency in programming languages such as Python (with data-related libraries: e.g. Pandas, Boto3), SQL, or Spark.
• Strong knowledge of RDBMS (e.g., Oracle, MS SQL Server) or NoSQL databases (e.g., MongoDB, ElasticSearch, GBQ).
• Experience with DevOps/DataOps practices, including CI/CD pipelines.
• Ability to implement data validation, lineage, documentation, and monitoring at scale.
• Proven ability to optimize pipelines and storage solutions for performance, cost, and reliability.

Qualifications

• Minimum 5 years of experience in data engineering, with a focus on cloud-native solutions and data architecture.
• English proficiency should be at least B2 level.

Required Skills

• Ability to evaluate trade-offs and make clear, defensible technical choices.
• Translate complex engineering decisions into clear, accurate explanations for non-technical stakeholders.
• Uplift and mentor mid/junior engineers, enforce standards, and foster a collaborative technical culture.
• Break down ambiguous, complex problems into clear, actionable steps and drive them to resolution.
• Take responsibility for outcomes, anticipate issues, and drive long-term platform evolution.