Data Engineer

Position Overview

We are looking for an experienced and versatileData 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 withFortune 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 withstrong business awareness and the ability to lead client and stakeholder communication. The ideal candidate will be comfortable collaborating withenterprise-level clients, translating complex technical concepts into business outcomes, and ensuring alignment between engineering execution and strategic objectives.

Job Responsibilities

• Design, build, and maintainscalable and reliable batch and real-time ETL/ELT data pipelines using cloud services such asGCP Dataflow, Cloud Functions, Pub/Sub, and Cloud Composer.

• Architect and implement robust data infrastructure capable of handlinghigh-volume data ingestion and processing.

• Develop and manage ourcentral data warehouse in Google BigQuery.

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

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

• Build reliable transformation workflows that supportanalytics, reporting, and data science initiatives.

• Monitor, troubleshoot, and optimize data infrastructure to ensurehigh performance, reliability, and cost efficiency.

• ImplementBigQuery best practices, includingpartitioning, clustering, query optimization, and materialized views.

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

• Ensure data is optimized and readily accessible forBI tools such as Looker and other analytics platforms.

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

• Establish processes fordata governance, observability, and lineage tracking.

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

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

• Partner with product teams and leadership to ensure thattechnical data solutions align with business strategy and client expectations.

• Takeownership of data platforms and architecture decisions, helping shape the future direction of our analytics and data infrastructure.

• Identify opportunities toimprove data reliability, automate workflows, and generate new insights through data.

• Contribute to a collaborative, high-performing engineering culture with strong communication and teamwork.

Basic Qualifications

• 5+ years of hands-on experience in data engineering, data integration, or data platform development.

• Degree inComputer Science, Engineering, Mathematics, or related STEM discipline.

• Strong programming and query skills inSQL and Python.

• Experience working withdistributed version control systems such as Git in anAgile/Scrum environment.

• Experience designing and orchestratingETL pipelines, particularly withDatabricks.

• Experience working withincloud environments (GCP, AWS, or Azure).

• Experience withdatabase systems such as MongoDB and Elasticsearch.

• Strong understanding ofdata warehousing and dimensional modeling methodologies.

• Hands-on experience withAirflow and Hadoop.

• Experience usingDocker for containerized workflows and reproducible environments.

• Ability to identify opportunities toimprove data quality, reliability, and automation.

• Strongbusiness awareness and communication skills, with the ability to collaborate with both technical teams and business stakeholders.

• Experience within theretail industry is a plus.

Preferred Qualifications

• Master’s degree in Computer Science, Engineering, or related discipline.

• Experience working withenterprise-scale data platforms and Fortune 500 clients.

• Familiarity withDruid and its Python API, includingKafka integrations.

• Strong experience usingApache Spark for large-scale data processing.

• Experience designingreal-time streaming data architectures.

• Experience working withAI-driven platforms, data infrastructure supporting AI/ML systems, or agentic AI workflows