Manufacturing Data Engineer

Ursus, Inc.; Foster City (CA); On-site; USD 94,000 - 107,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Commuter benefits

401K with company match

Job summary

Ursus, Inc. seeks a Manufacturing Data Engineer to design, build, and maintain data pipelines powering our Manufacturing Intelligence lakehouse at our Foster City, CA headquarters.

The role focuses on hands-on engineering with Databricks, Delta Lake, and dbt, delivering trusted data for KPIs and AI use cases. Responsibilities cover ingestion, modeling, quality, and CI/CD, with migration from Redshift to Databricks and close collaboration with data analytics, manufacturing ops, and platform

Qualifications

• Bachelor's degree in computer science, information systems, engineering, or related field.
• 6+ years in data engineering or related field.
• Strong SQL and Python (PySpark) proficiency.
• Production experience with Databricks, Delta Lake, and dbt.

Responsibilities

• Build batch and streaming ingestion into Delta Lake using Auto Loader and Lakeflow pipelines.
• Design and maintain bronze, silver, and gold data layers per MOPS standards.
• Create incremental dbt models on Databricks with MERGE/upsert patterns and SCD Type 2.
• Model manufacturing data to support KPIs, dashboards, and analytics.
• Process large, semi-structured data (JSON) with PySpark and Databricks SQL.
• Tune pipelines for cost and performance using partitioning, OPTIMIZE, VACUUM.

Skills

Strong SQL

Python (PySpark)

Databricks

dbt

Dimensional modeling

CDC loading

GitHub Actions

Terraform

AWS data services

S3

IAM

DMS

Kinesis

Education

Bachelor's degree in CS/IS/Engineering

Equivalent practical experience

Tools

Delta Lake

Unity Catalog

dbt

Databricks Workflows

Airflow

GitHub Actions

Job description

JOB TITLE: Manufacturing Data Engineer

LOCATION: Foster City, CA (Onsite)

PAY RANGE: $68 - $78/hr.

DURATION: 12 Months

POSITION SUMMARY:

The Client is seeking a Data Engineer (Contract) to design, build, and maintain the data pipelines and models powering our Manufacturing Intelligence data lakehouse. This is an onsite contract position based at our headquarters in Foster City, CA. You'll bring manufacturing data from on-prem databases, SAP MES, SAP HANA, test and diagnostics systems, etc., into the MOPS lakehouse, and shape it into trusted tables for KPIs, dashboards, and AI use cases. About 80% of the role is hands-on data engineering. The other 20% keeps the pipelines deployable, observable, and cost-efficient: CI/CD, AWS ingestion infrastructure, and MOPS-scoped Databricks configuration. You'll also work on our migration from Redshift to Databricks, and partner closely with the Data Engineering, Data Analytics, Manufacturing Operations, and Databricks platform team.

RESPONSIBILITIES:

• Build batch and streaming ingestion into Delta Lake using Auto Loader, Lakeflow Declarative Pipelines, Lakeflow Connect, or Structured Streaming, including CDC feeds from AWS DMS and SAP SLT.
• Design and maintain bronze, silver, and gold (medallion) layers that follow MOPS naming conventions and data dictionary standards.
• Build incremental dbt models on Databricks (dbt-databricks), using MERGE and upsert patterns, SCD Type 2, and handling for late or out-of-order records.
• Model manufacturing data for analytics, such as a single source of truth per unit (SFC), work orders, nonconformances, station results, and first-pass yield, and feed the semantic layer used by analysts.
• Process large and semi-structured data, such as JSON test payloads, using PySpark and Databricks SQL.
• Tune pipeline performance and cost through liquid clustering or partitioning, OPTIMIZE, VACUUM, file sizing, and right-sized job compute.
• Own data quality with dbt tests, Lakeflow expectations, and Lakehouse Monitoring, including freshness SLAs and alerting.
• Orchestrate workloads with Databricks Workflows or Airflow.
• Migrate existing Redshift models and pipelines to Databricks: translate SQL, run in parallel, reconcile results, and cut over.
• Partner with analysts and manufacturing stakeholders on requirements, and document lineage, data contracts, and table definitions in Unity Catalog.
• Build and maintain CI/CD for MOPS jobs, pipelines and dbt models with Databricks Asset Bundles and GitHub Actions, across dev, staging and prod.
• Manage MOPS-owned catalogs, schemas, volumes and grants in Unity Catalog, following the central platform team's governance model.
• Configure job compute and SQL warehouses within central cluster policies; manage MOPS service principals, secret scopes, and OAuth machine-to-machine auth for Airflow and dbt.
• Configure and troubleshoot the AWS ingestion layer that feeds the lakehouse (DMS, Kinesis, S3 landing buckets, IAM roles), partnering with infrastructure owners on provisioning through Terraform.
• Monitor job health and DBU spend using system tables and tagging, and set up alerting for failures and SLA misses.
• Document pipelines, runbooks, and operational procedures, and take part in incident response and root cause analysis for data pipeline issues.

QUALIFICATIONS:

• Strong SQL and Python, including PySpark.
• Hands-on production experience on Databricks: Delta Lake, Unity Catalog permissions, Workflows or jobs, and Spark performance tuning.
• Production experience with dbt, including incremental models and tests.
• Solid grasp of dimensional modeling, the medallion pattern, and CDC and incremental loading.
• Experience deploying data code through Git-based workflows and CI/CD (e.g., GitHub Actions, Databricks Asset Bundles or similar).
• Working knowledge of AWS data services (S3, IAM, DMS, or Kinesis) and infrastructure as code (e.g., Terraform).

PREFERRED QUALIFICATIONS:

• Databricks Certified Data Engineer Associate or Professional; AWS certification (e.g., Solutions Architect Associate) a plus.
• Experience preparing data for Databricks AI/BI dashboards, Genie, or ML and LLM use cases.
• Background in manufacturing or industrial data, such as quality, yield, traceability, or MES.
• Understanding of data governance and compliance as they relate to data pipelines.

EDUCATION:

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience, plus 6+ years in data engineering.

BENEFITS SUMMARY:

Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate or annual salary only, unless otherwise stated. In addition to base compensation, full-time roles are eligible for Medical, Dental, Vision, Commuter and 401K benefits with company matching.

IND123

BENEFITS SUMMARY:

Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate or annual salary only, unless otherwise stated. In addition to base compensation, full-time roles are eligible for Medical, Dental, Vision, Commuter and 401K benefits with company matching.

FAIR CHANCE POLICY:

Pursuant to the California Fair Chance Act, Los Angeles County Fair Chance Ordinance for Employers, Los Angeles Fair Chance Initiative for Hiring Ordinance, and San Francisco Fair Chance Ordinance, qualified applicants will be considered for assignment with arrest and conviction records. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness, meet client expectations, standards, and accompanying requirements, and safeguard business operations and company reputation.