Quality Engineering (QE) Data Quality Engineer

Amida Technology Solutions•, DC

22h•Hybrid

About The Position

Amida Technology Solutions is a DC-based technology company focused on data interoperability, integrity, governance, and security. We create solutions that collect, reconcile, transform, and standardize data for business intelligence, advanced analytics, decision support, and user transactions. We specialize in taking data from inception to impact. Our team is composed of creative, forward-looking thinkers who are passionate about using cutting-edge technology to improve lives and generate a positive impact on our country. We offer an entrepreneurial, high-growth environment that values fresh ideas, candid conversations, and authentic teamwork. Amida Technology Solutions is seeking a Quality Engineering (QE) Data Quality Engineer. The successful candidate will own the integrity of the data that flows through the test environments and analytics platform. This role will report to the Program Manager. The ideal candidate will be motivated by the opportunity to support public agencies, nonprofit organizations, and private companies in transforming their data into actionable insights. The candidate must be willing and able to work 3-4 days per week at our client's site in downtown Dallas, TX.

Requirements

At least three years of hands-on experience in test data management, data masking or de-identification, and database or data-layer validation
At least two years of data validation and reporting experience with Snowflake and Power BI or similar technologies
Practical experience with relational databases (e.g., PostgreSQL, SQL Server, Oracle, or MySQL) including schema design concepts, indexes, and constraints
Demonstrated ownership of a test data strategy: provisioning, refresh, subsetting, and environment-specific data governance
Experience validating data pipelines and warehouses and reconciling data across source and target systems
Advanced SQL including complex joins, window functions, aggregation, and query tuning against large tables
Proficiency with Jira/Xray, Confluence, GitHub Actions, and Azure DevOps as the shared baseline toolset across the QE team
Working knowledge of privacy and compliance requirements that drive masking (such as GDPR, CCPA, HIPAA, or PCI DSS) and the masking techniques that satisfy them (e.g., substitution, shuffling, tokenization, format-preserving encryption)
Scripting or programming ability in Python, or a comparable language, for automating validation and data generation
Familiarity with CI/CD tooling and version control, and comfort embedding data checks into automated builds
Ability to communicate clearly in writing, including defect reports, validation evidence, and documentation that a non-specialist can follow
Strong analytical and problem-solving skills

Nice To Haves

Experience with commercial or open-source TDM and masking tools (e.g., Delphix, Informatica TDM, IBM Optim, Tonic, or Redgate Data Masker)
Knowledge of data quality frameworks such as Great Expectations, dbt tests, Soda, or Deequ
Understanding of cloud data platforms such as Snowflake, BigQuery, Databricks, or Redshift
Experience with orchestration tooling such as Airflow, dbt, or Azure Data Factory
Streaming or event data validation with Kafka or an equivalent
Synthetic data generation for cases where production-derived data cannot be used at all
Security testing experience with Veracode or an equivalent
Mobile automation testing experience with BrowserStack, Percy, or equivalent platforms
Experience with UiPath or other RPA automation software
Familiarity with Selenium, Playwright, REST Assured, Postman/Newman, and OpenAPI/Swagger for UI, API, and contract testing
Prior work on federal, state, or local government programs

Responsibilities

Design, build, and maintain test data sets that cover realistic edge cases, referential integrity across systems, and volume profiles representative of production
Implement and operate data masking, subsetting, and synthetic data generation so that non-production environments carry no live personal or regulated data
Write and automate database-layer validation such as row counts, reconciliation, schema and constraint checks, referential integrity, slowly changing dimension behavior, and transformation logic
Validate ETL/ELT pipelines end to end, from source ingestion through staging and curated layers, and isolate defects to the responsible stage
Build reusable data quality frameworks and check suites, and wire them into CI/CD so data defects fail fast rather than surfacing in UAT
Define and track data quality metrics (e.g., completeness, accuracy, timeliness, uniqueness, consistency) and report trends to engineering and product leadership
Partner with developers, DBAs, data engineers, and product owners on test data requirements, refresh cadence, and environment readiness
Triage production data incidents, perform root cause analysis, and close gaps in coverage that allowed the defect through
Document data lineage, masking rules, and validation coverage so audits and onboarding do not depend on undocumented institutional knowledge
Other duties as assigned