Data Engineer III

sgs-job-5998

Engineering
Remote
Data Engineering
SQL
Python
ETL
TypeScript
Presto
Trino
Hive
Tableau

Contract - 12 Months

• Data Operations runs large-scale robot teleoperation and wearable data collection used to train robot manipulation models. The team builds the data pipelines, reporting infrastructure, and operator-facing tools that give the organization visibility into how much data is being collected, how efficiently, and at what quality. • We are hiring an experienced Data Engineer to design, build, and own the data models, pipelines, and reporting that this operation runs on. Today a meaningful amount of operational reporting is assembled by hand; a significant part of this role is replacing that with automated, trustworthy systems. • This is an embedded, high-visibility role rather than a ticket-queue role. You will work directly with robot operators, site leads, and program managers, translate their questions into durable data models, and own the numbers that appear in leadership reviews.

Job Responsibilities:

  • Design and own dimensional data models covering collection volume, operator efficiency, data quality, and equipment uptime across multiple collection methods.
  • Define and document canonical metric definitions so key terms mean one consistent thing across dashboards, internal apps, and leadership reporting — and drive alignment when teams disagree.
  • Build the daily and cumulative table layers that let recurring reporting be derived automatically instead of assembled manually.
  • Build and extend batch data pipelines that surface production and efficiency metrics from a multi-tier architecture (operational source systems → daily snapshots → cumulative dimension tables).
  • Own correctness, freshness, and backfill of every reported metric, including investigating and resolving discrepancies between reported figures and source data.
  • Support data validation frameworks, dataset assembly, and end-to-end data lineage tracking.
  • Build automated data-quality checks, anomaly detection, and freshness SLAs so data breaks are alerted rather than discovered in a meeting.
  • Own backfill and restatement strategy when a metric definition changes or an upstream source shifts, including communicating restatements to stakeholders.
  • Design for imperfect inputs — operator logs and manual trackers are a real data source today, and building the path that automates them away is part of the job.
  • Build and own dashboards and internal reporting views, including live fleet-status views and automatically generated daily operational briefs.
  • Build lightweight input interfaces so human context (status notes, staffing, blockers) sits alongside automated metrics.
  • Automate recurring operational workflows: daily production reporting, equipment status and maintenance tracking, and weekly metrics-review assembly.
  • Partner with site leads, lead operators, and program managers across two U.S. sites to turn operational questions into data models.
  • Support recurring operating cadences — daily production standup, weekly metrics review, and tracking against annual collection targets.
  • Deliver ad-hoc analysis for leadership (capacity, throughput per unit, downtime attribution) quickly and defensibly.

Skills:

  • Strong cross-functional partnership and communication — able to work with senior stakeholders up to director level, and to clarify, challenge, and push back constructively.
  • Experience setting up data architecture and pipelines for an organization, including dimensional modeling and ownership of production pipelines end to end (correctness, freshness, backfill).
  • Strong hands-on technical depth in the specific stack: Python, advanced SQL on very large partitioned tables in a distributed query engine, and pipeline orchestration.
  • Smaller company, startup, or pure-tech background — scrappier, with natural exposure to back-end systems and cross-team work.
  • Robotics, sensor, or time-series data experience (ROS bags, MCAP), or multimodal/unstructured data at scale.
  • Working knowledge of TypeScript/React, enough to extend an internal web app to surface new metrics.

Education/Experience:

  • BS in Computer Science, Data Engineering, or a related technical field.
  • 4+ years of professional data engineering experience.
  • Strong Python and strong SQL, including query performance on very large, partitioned tables in a distributed query engine (Presto, Trino, Spark SQL, BigQuery, Snowflake or similar).
  • Dimensional modeling and data warehouse design — star schema, slowly changing dimensions, incremental and cumulative table patterns.
  • Pipeline orchestration experience with scheduled workflows, dependency management, retries, and backfill (Airflow, Dagster, or an equivalent in-house scheduler).
  • Demonstrated ownership of a production data pipeline — accountable for correctness, freshness, and backfill, not just for building it.
  • Experience defining and documenting metrics as an organizational source of truth for a non-technical audience.
  • Experience building or owning dashboards in a BI tool (Tableau, Looker, Power BI, or an internal equivalent).
  • Comfortable working in a large, shared codebase with code review and CI.
  • Strong written communication and the ability to work directly with non-technical operational stakeholders in an ambiguous environment.
  • Experience in a physical operations, manufacturing, logistics, or fleet environment — uptime, utilization, yield, throughput, or downtime attribution. This transfers better to robotics operations than purely digital product analytics.
  • Working knowledge of TypeScript/React, enough to make contained changes to an existing internal web app to surface new metrics
  • Experience with robotics, sensor, or time-series data (for example ROS bags or MCAP).
  • Experience with multimodal or unstructured data at scale (video, sensor streams) and the metadata layer built over it.
  • Familiarity with large-scale object storage systems.
  • Prior experience supporting a data-collection, annotation, or labeling operations team.

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