Data Engineer

About the role

Position Overview

We are looking for an experienced and versatile Data Engineer to join our dynamic and fast-growing team. If you are passionate about data, solving complex problems, and working directly with enterprise stakeholders to translate business needs into scalable technical solutions, this role could be the perfect fit.

ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses across various industries by focusing on creating value through innovation.

In addition to strong technical expertise, we are seeking someone with strong business awareness and the ability to lead client and stakeholder communication. The ideal candidate will be comfortable collaborating with enterprise-level clients, translating complex technical concepts into business outcomes, and ensuring alignment between engineering execution and strategic objectives.

Job Responsibilities -

Design, build, and maintain scalable and reliable batch and real-time ETL/ELT data pipelines using cloud services such as GCP Dataflow, Cloud Functions, Pub/Sub, and Cloud Composer.

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Architect and implement robust data infrastructure capable of handling high-volume data ingestion and processing.

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Develop and manage our central data warehouse in Google BigQuery.

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Design and implement data models, schemas, and table structures optimized for performance, scalability, and long-term maintainability.

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Write clean, efficient, and maintainable SQL and Python code to transform raw data into curated, analysis-ready datasets.

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Build reliable transformation workflows that support analytics, reporting, and data science initiatives.

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Monitor, troubleshoot, and optimize data infrastructure to ensure high performance, reliability, and cost efficiency.

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Implement BigQuery best practices, including partitioning, clustering, query optimization, and materialized views.

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Build and maintain curated data models that serve as the “source of truth” for business intelligence and reporting.

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Ensure data is optimized and readily accessible for BI tools such as Looker and other analytics platforms.

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Implement automated data quality checks, validation rules, and monitoring frameworks to ensure the integrity and reliability of data pipelines and warehouse systems.

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Establish processes for data governance, observability, and lineage tracking.

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Work closely with software engineers, data analysts, and data scientists to understand their data requirements and provide the necessary infrastructure and data products.

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Lead and support client and stakeholder communication, working with enterprise clients to translate business needs into scalable data solutions.