DATA ENGINEER

DATAOPS LLC

United States

2 days ago

Apply on computerjobs.com

Prepare application

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

computerjobs.com

Tech stack

Agile Methodology Artificial Intelligence Data Analysis Behavior-Driven Development Big Data Cloud Computing Continuous Integration Information Engineering Data Governance Extract Transform Load (ETL) Data Transformation Data Warehousing

+15 more

Distributed Data Store Python (Programming Language) Machine Learning DataOps Software Engineering SQL Databases Data Streaming Google Cloud Test-Driven Development (TDD) Sql Optimization Snowflake Data Build Tool (dbt) Information Technology Google Bigquery Data Pipelines

Job description

Build Scalable Data Solutions - Design, build and support Data Models & distributed ETL pipelines using big data technologies on large scale data sets.
Deliver Impactful Data Features - Collaborate with our many stakeholders from multiple business areas to understand business and data challenges, thereafter, developing requirements, specifications and recommendations related to a proposed solution.
Champion DataOps & Best Practices - Drive data engineering excellence by embedding DataOps principles and best practices into everything you do - becoming a go-to expert in one or more data domains.
Solve Real-World Problems with Modern Tools - Tackle complex data challenges using tools like Google BigQuery, Python, SQL, and DBT, with opportunities to experiment and optimise through AI.
Lead & Mentor - Support and coach team members, sharing knowledge and acting as a role model while deputising for the Engineering Manager when needed.
Collaborate in Agile Squads - Work effectively across multiple GST workstream squads embedding as needed to support delivery and provide data engineering expertise.
Shape the Future of Data at Scale - Act as a bridge between various workstreams and the core SDP engineering organisation ensuring alignment to platform best practices, data governance, and architectural principles
Support workstream squads onboarding data feeds using contributing to strengthening collaboration and reducing mismatches with upstream systems.

Requirements

Data Analysis & Engineering Expertise - Proven experience designing and building complex data models and distributed data pipelines, with a strong focus on data analysis, insight generation, and solving real-world business problems at scale.
SQL Mastery - Advanced SQL skills for high-performance data transformation, exploration, and optimisation across large, complex datasets
Engineering Best Practices - Solid foundation in software and data engineering, including hands-on experience with CI/CD workflows, test-driven development, and agile collaboration.
Academic & Professional Credentials - Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
Cloud-Based ETL & Data Warehousing - Demonstrated success delivering large-scale data warehousing solutions, including dimensional modelling and ETL pipeline design-ideally within Google Cloud Platform (GCP) and good working knowledge of DBT (Data Build Tool) for building and managing data pipelines.
Modelling & Data Flow Understanding - Knowledge of how data is structured, processed, and applied within analytics and reporting environments.
Incident Support & Operational Readiness - Ability to support the team in diagnosing and resolving data-related incidents, ensuring platform reliability and timely issue resolution.
Academic & Professional Credentials - Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
Team Collaboration & Innovation - A natural collaborator with strong communication skills, capable of influencing technical decisions and contributing innovative ideas in a fast-paced, commercial environment.
Knowledge of Snowflake, iceberg and lake house architecture
Good to Have/Bonus Skills - Experience working with Customer and Commerce data, third-party vendor report integration, exposure to Behaviour-Driven Development (BDD) principles and familiarity with AI/ML concepts or practical experience applying AI in data workflows