Manager - Data Engineering

Role Purpose

The role involves designing and implementing robust data ingestion pipelines, enable SAS Viya Fraud, Waste, and Abuse (FWA) data models, and optimize large-scale data workloads in Google Big Query. The ideal candidates will have strong experience in ETL/ELT development using Informatica IDMC, cloud data platforms, and healthcare data models.

• Build reusable, parameterized mappings/taskflows.
• Implement CDC, idempotent loads, schema evolution, DQ gates.
• Optimize Big Query loads (partition/cluster, load vs. stream).
• Set up version control and CI/CD.

• Profile sources; define field-level mappings and rules.
• Specify business logic, joins, lookups, derivations.
• Define DQ rules, exceptions, and rejects.

• Parameterize taskflows; set schedules/dependencies.
• Implement retries/backoff and checkpointing.
• Integrate monitoring/alerts (Ops console, Chat Ops).

• Define least‑privilege IAM and service accounts.
• Apply dataset/table/row/column security & masking.
• Enable audit logs and retention; classify PHI/PII.

• Gather model inputs, features, windows, granularity.
• Align scoring logic and feature engineering steps.
• Design curated FWA datasets (claims, eligibility, providers, members, preauths).

• Track freshness, job health, volumes, anomalies.
• Monitor SLAs (duration, errors, cost/TB, slots).
• Tune Big Query.
• Optimize cost (storage lifecycle, query tuning, caching).