Overview

Algoworks is an award-winning artificial intelligence, engineering services and experience

Responsibilities

• We are seeking an ETL QA Data Engineer to ensure the accuracy, completeness, and
• reliability of data pipelines and analytics outputs across high-volume, event-driven
• domains such as emergency response, public safety, and healthcare operations. The role
• involves validating data ingestion and transformation logic, reconciling records between
• source and target systems, detecting anomalies, and ensuring reporting and dashboards
• reflect trustworthy data.
• Data Validation & Reconciliation
• Perform source-to-target reconciliation for event- and transaction-style datasets
• Validate completeness, timeliness, and consistency of daily and near-real-time
• Identify and investigate data anomalies, including missing records, spikes or drops,
• Validate the correctness of KPIs and metrics used in dashboards and reports,
• Ensure referential integrity across key entities such as events/encounters,
• Partner with engineering teams to debug discrepancies, identify root causes, and
• Automation & Framework Development
• Build and maintain automated data validation frameworks using Python and testing
• Develop reusable validation assets, including reconciliation templates, data quality
• Create data quality rules covering nulls, ranges, uniqueness, and referential
• Integrate validation into CI/CD and orchestration workflows, including validation
• Produce validation artifacts such as test reports, defect summaries, and data
• AI-Assisted Validation
• Use AI tools and LLM-based solutions, such as Qodo, to accelerate validation query
• Leverage AI-assisted techniques to identify edge cases, explore anomaly detection
• Validate all AI-generated outputs against actual data and documented business
• Data Quality & Reporting Reliability
• Ensure reporting layers and dashboards accurately reflect validated source data
• Identify and report data quality issues that may affect downstream analytics and
• Support the development of repeatable and documented validation rules aligned
• Contribute to early detection of anomalies and reduction of downstream reporting
• Collaboration & Continuous Improvement
• Collaborate with data engineering, analytics, and other relevant teams to

Qualifications

• Strong SQL skills for data reconciliation and validation, including joins,
• Hands-on Python experience for test automation and data validation.
• Experience in ETL/ELT QA, data testing, analytics validation, or data quality
• Understanding of event-driven and transaction-based systems and common
• Experience validating large datasets and reporting layers in environments with high
• SQL
• Python
• ETL/ELT QA
• Data Validation & Reconciliation
• Data Quality Testing
• Test Automation
• Source-to-Target Validation
• Data Anomaly Investigation

Nice to have

• Great Expectations or custom Python validation frameworks.
• dbt tests or similar data validation frameworks.
• Familiarity with cloud data platforms such as Snowflake, BigQuery, or Redshift.
• Exposure to Databricks or Spark.
• Familiarity with orchestration tools such as Airflow, Azure Data Factory (ADF), or
• Experience with sensitive data handling and privacy/security considerations.
• Exposure to interoperability and integration patterns such as APIs, HL7/FHIR, EDI,
• Familiarity with AI-assisted testing tools and LLM-based validation support.
• Strong analytical and problem-solving skills with attention to data accuracy and detail.
• Ability to investigate complex data discrepancies and identify root causes.
• A quality-focused mindset with an emphasis on data reliability and reporting integrity.
• Ability to collaborate effectively with engineering and analytics teams.
• Commitment to documenting validation rules and maintaining reusable testing assets.
• Sound judgment when using AI-assisted tools, with a focus on human verification.
• Automated validation coverage for critical pipelines, including freshness, completeness, and reconciliation.
• Early detection of anomalies and measurable reduction in downstream reporting issues.
• Repeatable, documented validation rules aligned with business definitions and metrics.
• Efficient collaboration with engineering teams to shorten incident triage and fix verification cycles.