{"id":1574311,"url":"https://alion.io/job/vatn-data-analysis-and-engineer","title":"Data Analysis and Engineer","company":{"id":720699,"name":"Vatn","domain":"vatn.com","url":"https://alion.io/company/vatn-2","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":7,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-05T05:45:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bristol, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":106000,"max_usd":229000,"period":"year","method":"role_country_seniority_unknown","sample_n":2605},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Grafana","optional":false},{"name":"MATLAB","optional":false},{"name":"NumPy","optional":false},{"name":"Plotly","optional":false},{"name":"Polars","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Streamlit","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"ClickHouse","optional":true},{"name":"Docker","optional":true},{"name":"DuckDB","optional":true},{"name":"GCP","optional":true},{"name":"InfluxDB","optional":true},{"name":"Machine Learning","optional":true},{"name":"PostgreSQL","optional":true},{"name":"QGIS","optional":true},{"name":"ROS","optional":true},{"name":"TimescaleDB","optional":true}],"status":"live","first_seen_at":"2026-09-30T17:00:23Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-06T00:51:59Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"About Vatn Systems:\nVatn Systems is an innovative startup at the forefront of designing and manufacturing autonomous underwater vehicles (AUVs). Our mission is to revolutionize underwater security, exploration, research, and operations through cutting-edge technology and unparalleled engineering. Our AUVs are defining the next generation of underwater autonomy.\nThe Role\nWe are looking for a talented and motivated Data Engineer / Analyst to join our growing team. Every vehicle we put in the water generates gigabytes of navigation, sonar, acoustic, and vehicle-health data, and today that data is underused. In this role, you will own the pipeline that turns raw mission logs into answers. The ideal candidate is a proactive individual who is comfortable owning data infrastructure end to end and who is as interested in the physics and purpose behind the data as in the plumbing that moves it.\nWhat You'll Do\nDesign, build, and maintain data pipelines that ingest post-mission logs from vehicles and field kits into a queryable, durable data store.\nDevelop reusable statistical analysis tooling in Python and/or Matlab for post-mission reconstruction, including coordinate frame handling, time alignment across asynchronous sensors, and navigation error characterization.\nBuild and maintain dashboards and reports that communicate vehicle and subsystem performance clearly to engineers, leadership, and customers.\nDefine and track quantitative performance metrics across programs and make trends visible over time.\nEstablish data schemas, naming conventions, and metadata standards so that test data is discoverable and traceable to a specific vehicle, build, and configuration.\nSupport field test campaigns by turning around rapid analysis between runs and flagging anomalies before the next deployment.\nBuild automated data quality checks and monitoring to catch dropped telemetry, clock issues, and sensor faults early.\nProduce analysis products that feed customer deliverables, test reports, and internal design reviews.\nWrite and maintain documentation and tests so that pipelines and analyses are reproducible by others on the team.\nWhat You'll Need\nRequired Qualifications:\nDegree in Engineering, Computer Science, Data Science, Applied Mathematics, Physics, or a related technical field (or equivalent practical experience).\nProfessional experience in data engineering, statistical analysis, or a comparable role.\nStrong proficiency in Python, Matlab and/or Rdata ecosystems (e.g., NumPy, pandas, Polars).\nStrong SQL skills and experience designing schemas for relational or time-series databases.\nExperience building and maintaining ETL/ELT pipelines that handle large, messy, real-world sensor or log data.\nFamiliarity with statistical methods, data transformation techniques, and data visualization.\nExperience building dashboards or visualization tooling (e.g., Grafana, Plotly/Dash, Streamlit).\nAbility to work with time-series data across multiple asynchronous sources, including timestamp alignment and resampling.\nProficiency with version control systems, specifically Git.\nExcellent problem-solving skills, strong written communication, and the ability to work effectively in a team environment.\nUnited States Citizen or Permanent Resident.\nAbility to gain a security clearance.\nNice-to-Haves:\nExperience with robotics data formats and middleware (e.g., ROS 2, rosbag/MCAP, DDS, Protobuf).\nExperience with leveraging machine learning for time-series and/or navigational data\nExperience with columnar and time-series storage formats and engines (e.g., Parquet, DuckDB, TimescaleDB, InfluxDB, ClickHouse).\nFamiliarity with geospatial data and coordinate systems (WGS84, UTM, local tangent plane / NED frames) and tools such as GeoPandas, PROJ, or QGIS.\nExperience with signal processing techniques (e.g., filtering, Fast Fourier Transforms, sensor data conditioning).\nExposure to navigation and state estimation concepts (INS, DVL, GNSS-denied operation, Kalman filtering).\nFamiliarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and infrastructure-as-code practices.\nKnowledge of containerization technologies like Docker, and experience with CI/CD pipelines.\nExperience with maritime sensors, underwater acoustics, or subsea systems.\nPrior experience at a startup and/or defense company.\nWillingness to participate in on-water testing and field operations as needed.\nVatn is an equal opportunity 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