Data Engineer III-6348

itD is seeking a Data Engineer to design, build, and own the data models, pipelines, and reporting behind a large-scale robot teleoperation and wearable data collection operation at a leading global technology company, replacing manual operational reporting with automated, trustworthy systems. The ideal candidate will bring deep experience in dimensional modeling, production pipeline ownership, and distributed SQL and Python, and a track record of delivering reliable reporting infrastructure and metrics that leadership trusts.

Location: Remote (US).

Pay Rate: $48.60/hr Depends on Experience

Duration: 12 months, full time (40 hours/week), start ASAP, with a strong possibility of extension or conversion.

We provide comprehensive medical benefits, a 401k plan, paid holidays, and more.
Please note that we are only considering direct W2 candidates at this time, as we are unable to offer sponsorship.

• →Design and own dimensional data models covering collection volume, operator efficiency, data quality, and equipment uptime across multiple collection methods, and build the daily and cumulative table layers that let recurring reporting be derived automatically.
• →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 and extend batch data pipelines from operational source systems through daily snapshots to cumulative dimension tables; own correctness, freshness, and backfill of every reported metric and resolve discrepancies against source data.
• →Build automated data-quality checks, anomaly detection, and freshness SLAs, and own backfill and restatement strategy (including communicating restatements to stakeholders) when definitions or upstream sources change.
• →Build and own dashboards and internal reporting views, including live fleet-status views and automatically generated daily operational briefs, plus lightweight input interfaces that pair human context with automated metrics.
• →Automate recurring operational workflows such as daily production reporting, equipment status and maintenance tracking, and weekly metrics-review assembly, replacing manual processes with trustworthy systems.
• →Partner directly with site leads, operators, program managers, and engineers across two U.S. sites to turn operational questions into durable data models, support daily and weekly operating cadences, and deliver defensible ad-hoc analysis for leadership.

• →Attend regular internal practice community meetings.
• →Collaborate with your itD practice team on industry thought leadership.
• →Complete client case studies and learning material (blogs, media material).
• →Build out material to contribute to the Digital Transformation practice.
• →Attend internal itD networking events (in person and virtual).
• →Work with leadership on career fast-track opportunities.

• BS in Computer Science, Data Engineering, or a related technical field.
• 5+ 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 equivalent).
• Demonstrated ownership of a production data pipeline, accountable for correctness, freshness, and backfill.
• 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 equivalent).
• Comfortable working in a large shared codebase with code review and CI.
• Strong written communication and the ability to partner with non-technical operational and senior stakeholders (up to director level) in an ambiguous environment.

• Experience in a physical operations, manufacturing, logistics, or fleet environment (uptime, utilization, yield, throughput, downtime attribution).
• Working knowledge of TypeScript/React, enough to make contained changes to an existing internal web app.
• Experience with robotics, sensor, or time-series data (e.g., ROS bags, 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.
• Startup or smaller-company background with exposure to back-end systems and cross-team work.

• BS in Computer Science, Data Engineering, or a related technical field required.