Senior Analytics Engineer
Remote
Applications have closed
Level Home
Keep your home's design & keys while adding Level's invisible smart locks. Explore unmatched craftsmanship, reliability & keyless entry options.This role is uniquely cross functional, as this decision support will sometimes span all the way from automating data acquisition to analysis. But that’s not all, a big part of this role is providing leadership in the process of discovery, design, and building tools you and the rest of the Data Team leverage to provide all of the above.
Responsibilities:- Create new data models, views, and data flows from a variety of sources to support product experimentation and device troubleshooting
- Collaborate with engineers across our stack to improve our telemetry collection capabilities and better inform downstream alerting metrics and troubleshooting tools
- Build data quality tests for our IOT device telemetry and ERP system including testing for data recency, cardinality, etc
- Lead product analytics standardization across web and mobile to take maximum advantage of both off the shelf product analytics tools and our internal analytics stack.
- Support business users through workshops, query and dashboard performance monitoring, creating upstream processing steps as needed. (Ex. aggregates, event sequencing/grouping, time series spines, etc)
- Build analytics on our analytics stack, everything from access auditing/monitoring to building qualitative insights on trends in inbound data requests.
- Advanced SQL skills to get the data you need from a data warehouse (e.g., BigQuery, Athena, Redshift) and perform data segmentation and aggregation from scratch
- Expert-level knowledge of SQL, dbt, BigQuery
- Data modeling and schema design
- Familiar with ETL/ELT tools (we use Airflow and Stitch)
- Experience in modern advanced analytical tools and programming languages such as Python
- Software engineering fundamentals and ability to write production-ready code
- Experience with version control systems (i.e. Git, Github) and workflows
- Experience working with data visualization tools such as Tableau, Data Studio, Looker, Mode, Metabase, Superset etc. (we use SigmaComputing)
- Knowledge of Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning)
- Cloud (AWS) & DevOps concepts (e.g., CICD), Software container technology (e.g., Kubernetes, Docker)
- Familiarity with Data Science workflow and life cycle
- Familiarity with Agile Methodologies and processes (we use Agile/SCRUM in JIRA)
- Understanding of CI/CD best practices (we have Github Webhooks -> dbt Cloud)
- Solid programming skills with Python data stack libraries and tools such as pandas, Jupyter Notebooks, Matplotlib, etc
Traits required for success in this role:
- Comfortable with ambiguity: You love tackling nebulous problems through discovery, experimentation and iteration
- Curious about People: You dive into nebulous problems and ask progressively better questions as you get more info about the problem being solved
- Curious about Process: You're curious about the minimal information artifacts which need to be captured from a business process or user interaction in order to run an experiment.
- Pragmatic about Maintainability: You take pride in the experience you provide to those who read your queries or extend your abstractions; an instinctive understanding of the tradeoff between time and succinctness shows in your work
Tags: Agile Airflow Architecture Athena AWS BigQuery CI/CD Computer Science Data quality Data Studio Data visualization Data warehouse DevOps Docker ELT Engineering ETL Finance Git GitHub Jira Jupyter Kubernetes Looker Matplotlib Metabase Pandas Python Redshift Scrum SQL Superset Tableau Testing
Perks/benefits: Flex vacation
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