ML Engineer

Tek Wissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation, information technology and services.

• Design and build Snowflake Semantic Views that define logical tables, relationships, dimensions, facts, and metrics for core network domains and datasets.
• Translate raw network counters into standardized KPIs aligned with 3
  
  GPP measurement definitions
• Model time-series behavior correctly. This includes granularity (5-min, 15-min, hourly, daily), time-zone handling, late-arriving data, and aggregation rules for additive, semi-additive, and ratio-based KPIs. For ratio KPIs, this means re-aggregating the numerator and denominator rather than averaging ratios.
• Build conformed dimensions such as network element, node type, vendor, region/site, time, and service/slice, so KPIs can be sliced consistently across domains.
• Author and maintain synonyms, descriptions, and sample questions so the semantic views work well with Cortex Analyst and natural-language querying.
• Work with the data engineering team to understand the ingestion architecture (e.g., Snowflake Kafka Connector, Snowpipe Streaming, Openflow, Dynamic Tables, Streams, Tasks etc.).
• Ensure the semantic layer is built on stable, well-defined interfaces rather than volatile raw tables.
• Implement automated drift detection covering schema changes (new, renamed, or dropped columns, and data type changes), new network elements or counters, and changes to KPI formulas.
• Use monitoring, Snowflake Alerts, and event-driven checks.
• Define and enforce data contracts between ingestion and semantic layers, including versioning, change notification, and backward-compatibility rules.
• Monitor data freshness and latency SLAs so that semantic views reflect near-real-time data. Configure Dynamic Table target lag or equivalent mechanisms appropriately.
• Manage semantic view definitions as code, with CI/CD pipelines for deployment across dev, test, and prod.
• Implement automated testing for KPI logic, including unit tests on formulas, row-count and null checks, threshold/anomaly tests, and regression tests against golden datasets.
• Maintain a KPI catalog and business glossary documenting each metric's definition, formula, source counters, owner, and change history.
• Optimize query performance and cost through clustering, warehouse sizing, materialization choices, and monitoring of compute consumption.
• Work closely with data engineers on ingestion design and with BI/analytics teams (Power BI, Tableau, etc.) who consume the semantic layer.
• Act as the point of contact for questions about metric definitions, resolving discrepancies across teams.

• Strong hands-on experience with Snowflake, including advanced SQL, Semantic Views and/or Cortex Analyst semantic models, Dynamic Tables, Streams and Tasks, Snowpipe/Snowpipe Streaming, and performance tuning.
• Proven experience building semantic layers or metrics layers. Relevant tools include Snowflake Semantic Views, dbt Semantic Layer/Metric Flow, AtScale, LookML, or Power BI semantic models.
• Solid dimensional modeling skills (Kimball star/snowflake schemas) and experience modeling high-volume time-series data.
• Working knowledge of telecom core network architecture familiarity with performance management counters and KPIs, is strongly recommended.
• Understanding of streaming data concepts:
  Kafka topics and partitions, event time vs. processing time, late and out-of-order data, and exactly-once semantics.
• Exposure to Python/Snowpark for automation and data quality frameworks.
• Strong analytical and communication skills, with the ability to explain technical metric logic to non-technical stakeholders.

Tek Wissen Group is an equal opportunity employer supporting workforce diversity.