Senior Data Engineer

Avaya

47d•$128,200 - $157,000

About The Position

You’ll build and scale the real-time and batch data platform that powers a large enterprise contact center solution. Our products demand ultra-low-latency decisioning for live interactions and cost-efficient big-data analytics for historical insights. We’re primarily on Azure today and expanding to GCP and AWS. Data is the backbone for our AI features and product intelligence. Primary charter: complex contact center analytics and operational intelligence: an AI-enabled enterprise contact center analytics. Our vision is a flexible AI-enabled data platform that unifies contact center KPIs, customer/business outcomes, and AI quality/performance, and pervasively applies AI to deliver advanced features that help users easily leverage rich contact center data alongside business data and AI performance monitoring to drive decisions end-to-end.

Requirements

6+ years building production-grade data pipelines at scale (streaming and batch).
Deep proficiency in Python and SQL; strong Spark experience on Databricks (or similar).
Advanced SQL: window functions, CTEs, partitioning/z-ordering, query planning and tuning in lakehouse environments.
Hands-on with Kafka (or equivalent) and an orchestrator (Airflow preferred).
Strong data modeling skills and performance tuning for low latency and high throughput.
Production mindset: SLAs, monitoring, alerting, CI/CD, and on-call participation.
Proficient using AI coding assistants (Cursor, Claude Code) as part of daily development.
Proficiency building data services/processors in Go (or willingness to ramp quickly), and familiarity with alternative frameworks (e.g., Flink/Beam) is a plus

Nice To Haves

Experience in multi-cloud or cloud migration (Azure plus either GCP or AWS).
Exposure to building data for AI/RAG, LLM-powered features, and agentic AI patterns (tool-use/function calling, planning/execution, memory).
Familiarity with LLMOps telemetry (prompt/response logs, token budgets) and agent evaluation pipelines.
Background in high-scale product engineering (vs. internal IT-only projects).
Contact center or CRM data familiarity (nice-to-have, not required).
Bachelor’s or Master’s in CS/EE/Math or similar; strong academic background and/or top-tier companies.

Responsibilities

Design, build, and operate low-latency streaming pipelines (Kafka, Spark Structured Streaming) and robust batch ETL/ELT on Databricks Lakehouse.
Establish reliable orchestration and dependency management (Airflow), with strong SLAs and on-call readiness for business-critical data flows.
Model, optimize, and document curated datasets and interfaces that serve analytics, product features, and AI workloads.
Implement data quality checks, observability, and backfills; drive root-cause analysis and incident prevention.
Partner with application teams (Go/Java), analytics, and ML/AI to ship data products into production.
Build and maintain datasets and services that power RAG pipelines and agentic AI workflows (tool-use/function calling).
When Spark/Databricks isn’t optimal, design and operate custom processors/services in Go to meet strict latency or specialized transformation requirements.
Instrument prompt/response and token usage telemetry to support LLMOps evaluation and cost optimization; provide datasets for labeling and golden sets.
Improve performance and cost (storage/compute), review code, and raise engineering standards.
Design data solutions aligned to enterprise security, privacy, and compliance requirements (e.g., SOC 2, ISO 27001, GDPR/CCPA as applicable), partnering with Security/Legal.
Implement RBAC/ABAC and least-privilege access; manage service principals, secrets, and key rotation; enforce encryption in transit and at rest.
Govern sensitive data: classification, PII handling, masking/tokenization, retention/archival, lineage, and audit logging across pipelines and storage.
Build observability for data security and quality; support incident response, access reviews, and audit readiness.
Embed controls in CI/CD (policy checks, dependency vulnerability scanning) and ensure infra-as-code adheres to guardrails.
Partner with security engineering on penetration tests, threat modeling, and red-team exercises; remediate findings and document controls.
Contribute to compliance audits (e.g., SOC 2/ISO 27001) with evidence collection and continuous control monitoring; support DPIAs/PIAs where required.