Senior Data Engineer

Tips: Provide a summary of the role, what success in the position looks like, and how this role fits into the organization overall.

Responsibilities

Proven experience architecting and implementing enterprise-scale data engineering solutions, including data pipelines, ETL/ELT frameworks, and data lake/warehouse architectures

Hands-on expertise with Dataiku for building, deploying, and operationalizing end-to-end data pipelines and ML workflows

Strong programming proficiency in Python for data transformation, automation, and pipeline orchestration

Advanced SQL skills for complex query optimization, data modeling, and working across relational/cloud data platforms

Solid working experience with AWS and Azure cloud ecosystems (e.g., S3/ADLS, Glue/Data Factory, Redshift/Synapse, IAM/security configurations) for building and managing scalable data infrastructure

Demonstrated ability to work at an Architect level — defining data architecture standards, evaluating tooling, and guiding technical decisions across cross-functional teams[Be specific when describing each of the responsibilities. Use gender-neutral, inclusive language.]

Example: Determine and develop user requirements for systems in production, to ensure maximum usability

Qualifications

[Some qualifications you may want to include are Skills, Education, Experience, or Certifications.]

Example: Excellent verbal and written communication skills

Skills: azure,sql,python,aws,dataiku