Senior Data Engineer

About Remote

Remote is solving global remote organizations’ biggest challenge: employing anyone anywhere compliantly. We make it possible for businesses big and small to employ a global team by handling global payroll, benefits, taxes, and compliance. Check out remote.com/how-it-works to learn more or if you’re interested in adding to the mission, scroll down to apply now.

Not only do we encourage folks from all ethnic groups, genders, sexuality, age, abilities, disability status and any other under-represented group to apply, but we prioritize a sense of belonging. We have 4 ERGs (Women, Disability, Queer, Minorities in Tech) who meet regularly with the People team. During your interviews and beyond, we ask & encourage anybody who needs an accommodation to request one from their recruiter.

All of our positions are fully remote. You do not have to relocate to join us!

What this job can offer you

This is an exciting time to join the growing Data Team at Remote, which today consists of over 15 Data Engineers, Analytics Engineers and Data Analysts spread across 10+ countries. Throughout the team we're focused on driving business value through impactful decision making. We're in a transformative period where we're laying the foundations for scalable company growth across our data platform, which truly serves every part of the Remote business. This team would be a great fit for anyone who loves working collaboratively on challenging data problems, and making an impact with their work. We're using a variety of modern data tooling on the AWS platform, such as Snowflake and dbt, with SQL and python being extensively employed.

This is an exciting time to join Remote and make a personal difference in the global employment space as a Senior Data Engineer, joining our Data team, composed of Data Analysts and Data Engineers. We support the decision making and operational reporting needs by being able to translate data into actionable insights to non-data professionals at Remote. We’re mainly using SQL, Python, Meltano, Airflow, Redshift, Metabase and Retool.

What you bring

• Significant experience modelling in popular data transformation frameworks (dbt, Airflow, etc.) and strong SQL proficiency
• Significant experience working with popular cloud data warehouses (Snowflake, Databricks, BigQuery, etc.)
• Strong knowledge of data modelling techniques (Kimball, Data Vault, etc.)
• Experience working with BI tools (Looker, Tableau, Sigma, Metabase, etc.)
• Strong affinity towards well crafted software - version control, testing, CI/CD (Gitlab, Github, etc.)
• A self-starter mentality and the ability to thrive in an unstructured and fast-paced environment
• Strong collaboration, documentation and communication skills
• Strong experience delivering large projects (with a significant data modelling component) autonomously
• Experience mentoring and coaching other members of Data teams
• Experience in dealing with ambiguity, working together with stakeholders on taking abstract concepts and turning them into data models that can answer a variety of questions
• You are a kind, empathetic, and patient person
• Write and speak fluent English
• Nice to haves: high-growth tech company experience, experience working remotely, software/data engineering experience, python development experience

Key responsibilities

• Data modelling:
  • Design, develop, and maintain dbt (Data Build Tool) models for data transformation and analysis, providing clean and reliable data models to end users enabling them to get accurate and consistent answers in our BI tools.
  • Able to deliver large projects autonomously, acting as the expert in all data modelling concerns, from project scoping through to execution
  • Collaborate with Data Analysts and business stakeholders to understand their reporting and analysis needs, translate them into concrete requirements, and deliver effective data solutions.
  • Own our internal dbt conventions and best practices, keeping our code-base clean and efficient (including code reviews for peers), and scaling the dbt project with the organisation.
• Data quality:
  • Ensure data quality and consistency by implementing data testing, validation and cleansing techniques.
  • Implement monitoring solutions to track the health and performance of the data present in our warehouse.
• Drive our culture of documentation:
  • Create and maintain data documentation & definitions, including data dictionaries and process flows.
  • Share knowledge and provide guidance to peers, creating an environment that empowers collective growth.