数据工程师

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.

Why You’ll Love Working at ShyftLabs

At ShyftLabs, your work matters. We’re a growing data product company making a big impact with Fortune 500 clients and as we scale, you’ll have the chance to shape solutions, influence strategy, and grow your career alongside us.

Here’s what you can expect when you join our team:

-Work Arrangement: This role is currently fully remote, providing flexibility to work from home. As the team and organization continue to grow, there may be an opportunity for the role to transition into a hybrid work model in the future, with occasional in-office collaboration.

-Comprehensive Benefits: We cover 100% of health, dental, and vision insurance premiums for you and your dependents which means no out-of-pocket costs. Eligibility starts from day one itself.

-Growth & Learning: Access extensive learning and development resources to keep leveling up your skills.

Inclusion at ShyftLabs

We’re building something big, and we want you on the journey with us. If you’re ready to use data and innovation to make an impact, apply today and let’s grow together.

ShyftLabs is an equal-opportunity employer committed to creating a safe, diverse, and inclusive environment. We encourage applicants of all backgrounds including ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, and nationality to apply. If you require accommodation during the interview process, let us know and we’ll be happy to support you.

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.

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

• Develop and manage our central data warehouse in Google BigQuery.

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

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

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

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

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

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

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

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

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

• Work closely with software 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 that technical data solutions align with business strategy and client expectations.

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

• Identify opportunities to improve 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 in Computer Science, Engineering, Mathematics, or related STEM discipline.

• Strong programming and query skills in SQL and Python.

• Experience working with distributed version control systems such as Git in an Agile/Scrum environment.

• Experience designing and orchestrating ETL pipelines, particularly with Databricks.

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

• Experience with database systems such as MongoDB and Elasticsearch.

• Strong understanding of data warehousing and dimensional modeling methodologies.

• Hands-on experience with Airflow and Hadoop.

• Experience using Docker for containerized workflows and reproducible environments.

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

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

• Experience within the retail industry is a plus.

Preferred Qualifications

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

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

• Familiarity with Druid and its Python API, including Kafka integrations.

• Strong experience using Apache Spark for large-scale data processing.

• Experience designing real-time streaming data architectures.

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