Senior Analytics Engineer

Summary

Join Ambient as a Senior Analytics Engineer and contribute to data-driven decision-making across the organization. You will collaborate with various teams, from product and engineering to operations and finance, to build and improve data tools and models. This role involves creating data flows, collaborating on telemetry improvements, building data quality tests, and supporting business users. The position requires advanced SQL skills, data modeling experience, and familiarity with cloud-based data ingestion. A small team setting offers high visibility and significant ownership. If you thrive in ambiguous environments and enjoy a high-impact role, this opportunity is for you.

Requirements

• Advanced SQL skills to get the data you need from a data warehouse (we use BigQuery)
• Data modeling and schema design skills (we use dbt)
• Experience developing cloud hosted data ingestion web services (we’re in AWS and GCP) including code running as lambdas or in containers (e.g. Kubernetes, Docker)
• Ability to match observability concepts (e.g. traces, logs, metrics) to observability needs (e.g. first tier support vs. QA reproducibility)
• 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)

Responsibilities

• Create new data models, views, and data flows from a variety of sources to support use cases ranging from 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
• 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

Preferred Qualifications

• 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)
• Familiarity with Data Science workflow and life cycle
• Solid programming skills with Python data stack libraries and tools such as pandas, Jupyter Notebooks, Matplotlib, etc