Data Quality Engineer

Job Description

DATA QUALITY / OBSERVABILITY ENGINEER MUST BE IN SEATTLE WASHINGTON

Role Overview

The Data Quality / Observability Engineer is responsible for designing, implementing, and maintaining data quality controls, pipeline observability, monitoring frameworks, and reliability mechanisms across enterprise data platforms. The role focuses on ensuring data pipelines are accurate, complete, timely, traceable, and operationally stable for analytics, reporting, and business-critical consumption.

You will work closely with Data Engineers, Architects, DevOps teams, Analysts, and business stakeholders to build robust validation frameworks, monitoring dashboards, alerting mechanisms, and operational controls that improve trust, visibility, and reliability across the data ecosystem.

Key Responsibilities

· Design and implement data quality frameworks for structured, semi-structured, and streaming data pipelines

· Define and enforce data validation rules such as completeness, schema conformance, uniqueness, freshness, reconciliation, and anomaly detection

· Develop reusable data quality checks for ingestion, transformation, and consumption layers

· Build and maintain observability mechanisms for pipeline health, throughput, lag, latency, failures, retries, and SLA monitoring

· Implement monitoring dashboards, alerting rules, and health metrics for batch, CDC, and streaming pipelines

· Configure and manage data quality exception handling, quarantine patterns, and failure workflows

· Design and support operational runbooks, incident diagnostics, restart procedures, backfill validation, and reliability controls

· Collaborate with Data Engineers to embed data quality checks into ETL/ELT pipelines and orchestration workflows

· Implement end-to-end lineage visibility, metadata tracking, and auditability across data movement processes

· Support root-cause analysis for data issues, pipeline failures, SLA breaches, and unexpected data anomalies

· Establish SLA / SLO / KPI monitoring for data freshness, completeness, delivery success, and operational performance

· Automate data health checks and integrate them into CI/CD and deployment validation processes

· Work with governance and business teams to align data quality thresholds, rules, and acceptance criteria

· Support production monitoring, incident triage, and operational issue resolution for critical data pipelines

· Optimize monitoring and alerting configurations to reduce noise, false positives, and operational overhead

· Maintain technical documentation for DQ rules, observability standards, dashboards, alerts, incident workflows, and operational procedures

Required Skills

· Strong experience in data quality, observability, and pipeline monitoring

· Hands-on experience with SQL and Python

· Good understanding of ETL/ELT pipelines, batch, and streaming workflows

· Experience with monitoring and dashboarding tools such as Grafana, CloudWatch, Azure Monitor, or similar

· Understanding of data validation, schema checks, reconciliation, and SLA monitoring

· Familiarity with lineage, metadata, and governance concepts

Preferred Skills

· Experience with Great Expectations, Deequ, dbt tests, Monte Carlo, Soda, or similar tools

· Exposure to Airflow, Spark, Kafka, Databricks, or cloud-native data platforms

· Experience with incident playbooks, alert tuning, and operational support

Skills Required

AWS Cloudformation

Role

• Data Analytics associated with AWS Cloud platform and services, ETL, Implementation of Data Pipelines/Connectors for Data Extraction
• batch and streaming pipelines, Implementing data ingestion patterns (CDC, append-only, merge/upsert)
• Data modeling: Star Schema, Snowflake, Data Vault
• Working with structured, semi-structured (JSON, Parquet, Avro) and unstructured data
• Metadata management and Data Cataloging
• Programming: Python, Shell Scripting, SQL Queries, Scala ( Desirable)

Location

Seattle, Washington