Senior Software Engineer II (Data Pipelines)
Summary
Senior data engineer who designs, automates, and optimizes ELT data pipelines for Wpromote's Polaris marketing intelligence platform, leading architecture, CI/CD, and pipeline performance work while mentoring other engineers. Core stack: Python, SQL, Airflow, dbt, and Google Cloud (BigQuery), with Kubernetes, Terraform, and Looker.
As a Senior Engineer, you will play a crucial role in the development, optimization, and automation of our data pipelines. You will lead complex data projects, architect technical initiatives, and ensure the scalability and performance of our data solutions. Your role will involve strategic planning, collaboration with senior management, and mentoring other engineers. You will be responsible for pushing the delivery speed and quality of our data ELT, leading the implementation of innovative tooling and architecture patterns, and fostering a culture of data engineering excellence.
At Wpromote, we're developing something special called Polaris, a modern data and marketing intelligence platform that gives our clients a serious edge over their competition. Polaris enables and empowers our marketing teams to work together seamlessly, using a comprehensive marketing data foundation with apps that deliver new insights and performance gains for our clients. Polaris also gives Wpromote an operational edge, doubling as our company CRM and integrated source of truth for operations data, making our lives easier and our decisions smarter.
You Will Be
- Acting as a technical leader and Subject Matter Expert (SME) in data pipeline automation and workflow orchestration.
- Designing, implementing, and maintaining complex, reliable, data solutions with a focus on automation using Airflow, dbt, and Google Cloud data products.
- Managing a large portfolio of dbt models, leveraging macros and DRY patterns.
- Advocating for test-driven development and assisting QA in developing a robust and reliable process for continuous integration and delivery.
- Monitoring, troubleshooting, and optimizing the performance of data pipelines and workflows.
- Architecting solutions and reusable patterns that scale with business needs.
- Providing implementation, configuration, and deployment documentation.
- Proactively addressing issues and problems, generating and implementing innovative solutions.
- Participating in all agile ceremonies, including daily standups and regular sprint planning.
- Mentoring other engineers and fostering a culture of technical excellence.
- Staying up-to-date with the latest industry trends and technologies to drive continuous improvement and innovation in data engineering practices.
- Ensuring data security, governance, and compliance with relevant standards and privacy restrictions.
You Must Have
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Minimum 7+ years of experience in software development, with extensive experience in Python, SQL, and data pipelines.
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Expertise in building and automating ETL/ELT pipelines using Airflow and DAG-based workflow management software. Airbyte for data ingestion is a plus, but not required.
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Deep experience managing dbt models using macros and dbt tests.
Mastery of Python or comparable scripting language, API integrations, and software architecture. -
Deep experience with databases like PostgreSQL and BigQuery, including query optimization for performance and cost.
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Good understanding of business intelligence tools like Looker or comparable alternatives.
Experience with Google Cloud Platform, Kubernetes, and managing infrastructure as code using Terraform. -
Proficiency with advanced data formats (Parquet, Avro, Hive, JSONL) and data integration techniques.
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Experience with monitoring and logging tools (e.g., Prometheus, Grafana) is a plus.
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Familiarity with version control systems and CI/CD tools like GitHub Actions.
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Strong command of agile methodologies, continuous integration, and test-driven development.
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Exceptional problem-solving skills and technical leadership.
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Ability to influence and guide cross-functional teams and projects.
Skills
- Agile
- Airbyte
- Airflow
- API
- Automation
- BigQuery
- CI/CD
- Cloud
- CRM
- Data Engineering
- Data Ingestion
- Data Pipelines
- dbt
- ELT
- ETL
- GCP
- GitHub
- GitHub Actions
- Grafana
- Hive
- Infrastructure as Code
- Kubernetes
- Looker
- Parquet
- PostgreSQL
- Prometheus
- Python
- SQL
- Strategic Planning
- TDD
- Terraform
- Version Control
- Workflow Orchestration
As published by lever · 10 questions · 1 written answer
Basics
Resume/CV, Full name, Pronouns, Email, Phone, Current location, Current company, LinkedIn URL, Portfolio (if applicable) URL, Other website, What is your age range?, I identify my ethnicity asSelect all that apply, What gender do you identify as?
Short answers (1)
- Desired Salary
Pick from a list (8)
- Earliest Start Date? optional
- How Did You Hear About Us? optional
- Are you legally authorized to work in the United States? optional
- Will you now or in the future require sponsorship for employment visa status (e.g. E, F-1 STEM OPT, H-1B, J-1, L-1, O-1, TN) optional
- What state do you currently reside in? optional
- How many years of experience do you have building production data pipelines? optional
- Have you personally designed, built, and operated data pipelines using Airflow and dbt with more than 1,000 DAGs in production? optional
- Have you built cloud-based data pipelines using GCP, AWS, or Azure, including supporting data quality issues or production incidents? optional
Written answers (1)
- In 150 characters or fewer, What’s a performance or growth challenge you love tackling—and how do you approach it? optional
