Data Engineer
Position Overview
Data Engineer
Fortune 500 companies
strong business awareness and the ability to lead client and stakeholder communication
enterprise-level clients
Job Responsibilities
- Design, build, and maintain
scalable and reliable batch and real-time ETL/ELT data pipelines
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
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
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
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
Agile/Scrum environment
.
- Experience designing and orchestrating
ETL pipelines
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
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
Kafka integrations
.
- Strong experience using
Apache Spark
for large-scale data processing.
- Experience designing
real-time streaming data architectures
.
- Experience working with
Why You’ll Love Working at ShyftLabs
-Work Arrangement
fully remote
transition into a hybrid work model in the future
-Comprehensive Benefits
-Growth & Learning
Inclusion at ShyftLabs
Not included in the source posting: about the role.
Skills
Who can apply
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