The Data Engineer will evaluate data infrastructure, optimize data pipelines, and collaborate with teams to enhance data observability and governance.
Overview
We are seeking a skilled Data Engineer to support a comprehensive Data Modernization Assessment for our client. This role will play a critical part in evaluating and optimizing the data architecture, pipelines, and observability infrastructure. The ideal candidate brings hands-on experience building scalable data systems, a deep understanding of cloud-based ecosystems, and a collaborative approach to working with technical and business teams.
Job Responsibilities
- Support the review and analysis of the client’s end-to-end data infrastructure, with a focus on scalability, maintainability, and performance.
- Evaluate and optimize existing data pipelines and orchestration workflows, ensuring reliability and efficiency.
- Collaborate with architects and the Technical Product Manager to assess architecture decisions, contributing technical insights and implementation feasibility.
- Build proof-of-concept improvements to support enhanced observability and monitoring, including data lineage, logging, and alerting.
- Assist in evaluating the data governance and metadata management frameworks, identifying pain points in data quality and accessibility.
- Contribute to mapping and enhancing the analytics tooling landscape, ensuring proper integration across reporting and dashboard platforms.
- Work alongside AI/BI specialists to assess the organization’s data readiness for AI-driven analytics and agentic BI use cases.
- Partner with cross-functional teams including data scientists, platform engineers, and business stakeholders to ensure technical alignment and data usability.
- Participate in the development of strategic recommendations and implementation roadmaps, supporting technical documentation and delivery planning.
Basic Qualifications
- 4–6 years of hands-on experience as a Data Engineer or equivalent role within enterprise-scale data environments.
- Strong expertise in building and optimizing ETL/ELT pipelines using tools such as Apache Spark, Databricks, Airflow, dbt, or equivalent.
- Proficiency in working with cloud data platforms (e.g., AWS, Azure, GCP), including data lake and warehouse solutions.
- Experience with observability and monitoring tools such as Datadog, Prometheus, or OpenTelemetry.
- Solid understanding of data governance concepts, metadata cataloging, and quality frameworks.
- Familiarity with analytics and visualization tools such as Power BI, Tableau, or Looker.
- Strong problem-solving, documentation, and communication skills; able to explain technical topics to non-technical audiences.
Preferred Qualifications
- Exposure to agentic AI systems, LLM-powered analytics, or modern BI automation platforms.
- Experience working in regulated or enterprise client environments, particularly during digital transformation or modernization initiatives.
- Familiarity with version control (e.g., Git), CI/CD for data systems, and infrastructure-as-code practices.
We are proud to offer a competitive salary alongside a strong healthcare insurance and benefits package. The role is preferably hybrid, with 2 days per week spent in the office, and flexibility for client engagement needs. We pride ourselves on the growth of our employees, offering extensive learning and development resources.
ShyftLabs is an equal-opportunity employer committed to creating a safe, diverse and inclusive environment. We encourage qualified applicants of all backgrounds including ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, nationality, and education levels to apply. If you are contacted for an interview and require accommodation during the interviewing process, please let us know.
Top Skills
Airflow
Spark
AWS
Azure
Databricks
Datadog
Dbt
GCP
Git
Looker
Opentelemetry
Power BI
Prometheus
Tableau
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