Manager, Data Validation (Data QA) (Remote/Contract)

About Annalect Annalect is the Data & Technology arm of Omnicom Media Group Annalect’s 4,000+ innovators leverage data and technology to help clients across Omnicom build relationships that matter — whether that means fostering consumers’ trust in brands, building new experiences, or delivering advanced analytics where it’s most needed. Annalect is the driving force behind Omni, Omnicom’s unique open operating system, which works hand-in-hand with clients’ and partners’ data and tools, to orchestrate better marketing outcomes. Annalect’s unique approach to data and technology – one that relies on transparency, neutrality, and interoperability – allows us to deliver purpose-built and scalable solutions that make data actionable. Our advanced teams of product leaders, data scientists, consultants, and engineers enable us to meet the business goals of our internal and external clients. Omnicom, Annalect's parent company, is poised to become the world’s Marketing & Advertising industry leaders in 2025. This means unprecedented growth, new and innovative solutions for our international brand partners, and exciting career opportunities for media and technology professionals! Position Overview We have an immediate need for a Manager, Data QA and are currently seeking individuals who are interested in long-term independent contractor engagements (on 1099 status) who would potentially consider full-time, permanent roles with us in the future. Working remotely is an option for this role during the contractor period. However, you will need to be based in the United States and available during East Coast Time Zone working hours. Annalect is seeking a hands-on Manager to lead and elevate data quality assurance practices across our growing suite of software and data products. This is a technical leadership role embedded within our Technology teams, focused on establishing best-in-class data quality processes that enable trusted, scalable, and high-performance data solutions. The Data QA team ensures that the data ingested from internal and external platforms is accurate, complete, and aligned with expectations. As a Manager on the Data QA team, you will drive the design, implementation, and continuous improvement of end-to-end data quality frameworks, with a strong focus on automation, validation, and governance. You will work closely with data engineering, product, and analytics teams to ensure data integrity, accuracy, and compliance across complex data pipelines, platforms, and architectures, including Data Mesh and modern cloud-based ecosystems. This role requires deep technical expertise in SQL, Python, data testing frameworks like Great Expectations, data orchestration tools (Airbyte, DbT, Trino, Starburst), and cloud platforms (AWS, Azure, GCP). You will lead a team of Data QA Engineers while remaining actively involved in solution design, tool selection, and hands-on QA execution. Key Responsibilities • Develop and implement a comprehensive data quality strategy aligned with organizational goals and product development initiatives. • Ensures that ingested data is complete, accurate, and conforms to expectations • Define and enforce data quality standards, frameworks, and best practices, including data validation, profiling, cleansing, and monitoring processes. • Establish data quality checks and automated controls to ensure the accuracy, completeness, consistency, and timeliness of data across systems. • Collaborate with Data Engineering, Product, and other teams to design and implement scalable data quality solutions integrated within data pipelines and platforms. • Define and track key performance indicators (KPIs) to measure data quality and effectiveness of QA processes, enabling actionable insights for continuous improvement. • Generate and communicate regular reports on data quality metrics, issues, and trends to stakeholders, highlighting opportunities for improvement and mitigation plans. • Maintain comprehensive documentation of data quality processes, procedures, standards, issues, resolutions, and improvements to support organizational knowledge-sharing. • Provide training and guidance to cross-functional teams on data quality best practices, fostering a strong data quality mindset across the organization. • Lead, mentor, and develop a team of Data QA Analysts/Engineers, promoting a high-performance, collaborative, and innovative culture. • Provide thought leadership and subject matter expertise on data quality, influencing technical and business stakeholders toward quality-focused solutions. • Continuously evaluate and adopt emerging tools, technologies, and methodologies to advance data quality assurance capabilities and automation. • Stay current with industry trends, innovations, and evolving best practices in data quality, data engineering, and analytics to ensure cutting-edge solutions. Required Skills • Hands on experience with data visualization and reporting tools (e.g., Tableau, Power BI, Looker) for surfacing data quality metrics and dashboards. • Strong proficiency in SQL for complex data querying, data validation, and data quality investigations across relational and distributed databases. • Deep knowledge of data structures, relational and non-relational databases, stored procedures, packages, functions, and advanced data manipulation techniques. • Practical experience with leading data quality tools such as Great Expectations, DbT tests, and data profiling and monitoring solutions. • Experience with data mesh and distributed data architecture principles for enabling decentralized data quality frameworks. • Hands-on experience with modern query engines and data platforms, including Trino/Presto, Starburst, and Snowflake. • Experience working with data integration and ETL/ELT tools such as Airbyte, AWS Glue, and DbT for managing and validating data pipelines. • Strong working knowledge of Python and related data libraries (e.g., Pandas, NumPy, SQLAlchemy) for building data quality tests and automation scripts. • Experience in testing data pipelines using workflow orchestration tools such as Apache Airflow, Dagster, or Prefect. • Proven experience with cloud data ecosystems — primarily AWS (EC2, EMR, RDS, Redshift, Athena) and familiarity with Azure or GCP as an added advantage. • Proficiency in CI/CD tools, Git/Bitbucket, and DevOps best practices to manage and deploy QA pipelines seamlessly. • Understanding of data security, privacy, and compliance standards, ensuring QA processes align with regulatory frameworks (e.g., GDPR, CCPA). • Proven leadership experience managing and mentoring Data QA/Engineering teams, including setting quality standards, performance reviews, and career development. • Strong project management experience, including planning, executing, and monitoring complex data quality initiatives. • Excellent communication and collaboration skills, capable of working cross-functionally with Engineering, Data Science, Product, Marketing and Business teams. • Strong analytical and problem-solving abilities for root cause analysis and implementing preventive QA measures. • 10+ years of relevant experience in Data Quality Assurance, Data Test Automation, Data Comparison, and Validation across large-scale datasets and platforms. Nice to have Skills • Knowledge of Marketing platforms across Social, Search, and Programmatic advertising, including Google Ads, Meta (Facebook & Instagram) Ads, Amazon Ads, The Trade Desk, DV360, and other DSPs, with an understanding of how marketing data flows through these platforms. • Experience with Marketing Analytics platforms (e.g. Adverity, Domo, Datorama (MCI), Supermetrix etc.) • Knowledge of data catalog and governance tools (e.g., Secoda, Alation, DataWorld etc.) to manage metadata and lineage for quality assurance. • Exposure to machine learning techniques and AI to detect and predict data anomalies and support advanced data quality use cases. • Experience with multi-cloud environments (AWS, Azure, GCP) and associated data services, orchestration, and monitoring tools. • Understanding of data security principles, encryption, and access control to ensure secure handling of sensitive data within QA processes. In our application, you will have the opportunity to propose your hourly independent contractor rate. If you are interested in exploring the world of Omnicom, we encourage you to begin your journey by applying now! This role is hybrid, requiring three (3) days per week in the office. The remaining two (2) days may be worked remotely. Specific in-office days will be discussed during the interview process, with flexibility to align with team needs. Please note that the number or required in-office days may be adjusted over time, potentially increasing the number of required in-office days based on business needs. Review Our Recruitment Privacy Notice