Key Responsibilities Data Cleansing and Profile Deduplication Audit customer profile data across loyalty platforms to identify duplicate, fragmented, or corrupted records Design and execute profile merge logic using deterministic and probabilistic matching techniques Develop and apply data cleansing routines to normalize, standardize, and enrich customer records Document merge decisions, audit trails, and remediation outcomes for stakeholder review Automated Data Quality Checks Build and maintain automated validation pipelines to detect data sync failures, schema drift, and referential integrity violations across platforms Develop scheduled reconciliation jobs that compare and validate records across mParticle, Braze, Xenial Beanstalk, GiveX, Snowflake, and Azure SQL Implement alerting and reporting to surface data anomalies in near real-time Own the end-to-end lifecycle of automated quality checks from design through deployment, monitoring, and iteration Continuous Monitoring and Observability Establish data quality KPIs and dashboards to track health metrics across the loyalty data ecosystem Monitor platforms continuously for consistency, completeness, accuracy, and timeliness of data Proactively identify and escalate data quality risks before they impact customer-facing loyalty experiences Maintain runbooks and documentation for monitoring processes and remediation procedures Cross-Platform Integration Validation Validate data flows between loyalty platforms via REST APIs and event-driven pipelines Perform integration testing to confirm data fidelity across ingestion, transformation, and consumption layers Collaborate with engineers, system owners, and platform vendors to trace and resolve upstream data issues Exciting Projects: We focus on industries like High-Tech, communication, media, healthcare, retail and telecom.
Our customer list is full of fantastic global brands and leaders who love what we build for them.
Collaborative Environment: You Can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment — or even abroad in one of our global centres.
Work-Life Balance: Accellor prioritizes work-life balance, which is why we offer flexible work schedules, opportunities to work from home, and paid time off and holidays.
Professional Development: Our dedicated Learning & Development team regularly organizes Communication skills training, Stress Management program, professional certifications, and technical and soft skill trainings.
Excellent Benefits: We provide our employees with competitive salaries, family medical insurance, Personal Accident Insurance, Periodic health awareness program, extended maternity leave, annual performance bonuses, and referral bonuses.
Disclaimer: - Accellor is proud to be an equal opportunity employer.
We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic 5+ years of experience in data quality engineering, data engineering, or a directly related discipline Proficiency in SQL with hands-on experience querying and manipulating data in Azure SQL and Snowflake Demonstrated experience creating test scripts, test plans, and writing automation scripts and data pipelines in C# (.
NET), Python, or both Practical experience working with REST APIs for data validation and cross-system reconciliation Proven experience with customer profile deduplication, identity resolution, or data cleansing at scale Working familiarity with Azure cloud services and cloud-native data architectures Ability to work independently, self-manage priorities, and deliver findings with minimal oversight Experience with loyalty platforms, CDPs, or MarTech ecosystems, specifically mParticle, Braze, Xenial Beanstalk, GiveX Familiarity with identity resolution concepts including deterministic and probabilistic matching Experience building or operating data observability and monitoring frameworks Background in QSR, retail, or high-volume consumer-facing platform environments Knowledge of data privacy regulations (CCPA, GDPR) and consent management best practices
