About Quantzig -

Quantzig is a global analytics and technology solutions company helping organizations leverage Data, AI, Analytics, and Digital Transformation to solve complex business challenges. We work with leading enterprises across industries to deliver data-driven solutions that create measurable business impact.

We are seeking a hands-on Data Engineer with strong Master Data Management experience, specifically on Reltio, to build and operate the trusted-data backbone of an enterprise analytics platform. You will design and implement MDM pipelines across Reltio and Databricks ingesting source data, standardizing and enriching it, running match-and-merge logic, and publishing golden records that downstream analytics, reporting, and AI products depend on.

This is an engineering role, not a configuration-only one. You will write production-grade Spark and SQL, own data-quality rules end to end, and work directly with data stewards, architects, and client stakeholders to turn messy multi-source data into a single, governed version of the truth

Role & responsibilities -

Master Data Management Engineering

- Build and maintain MDM pipelines across core domains customer, product, vendor, HCP/HCO, location from source ingestion through to published golden records.
- Implement and tune matching, survivorship, and merge/unmerge logic, including deterministic and probabilistic match rules, thresholds, and confidence scoring.
- Design standardization, cleansing, normalization, and enrichment routines (address, name, and reference-data harmonization) ahead of the match step.
- Build and maintain hierarchies, cross-reference (XREF) mappings, and source-to-master lineage so any golden record can be traced back to its contributing sources.
- Support data stewardship workflows exception queues, manual review, and steward-driven overrides and feed steward decisions back into rule tuning.

Databricks & Lakehouse Development (Great if you have databricks/Not Mandatory)

- Develop scalable ETL/ELT pipelines on Databricks using PySpark, Spark SQL, and Delta Lake across bronze/silver/gold layers.




- Build incremental and CDC-based ingestion patterns, and use Delta features MERGE, time travel, schema evolution, Z-ordering appropriately.
- Orchestrate workflows with Databricks Workflows or an enterprise scheduler, with clear dependency, retry, and alerting behaviour.
- Tune Spark jobs for cost and performance: partitioning strategy, broadcast joins, skew handling, cluster sizing.
- Apply Unity Catalog governance catalogs, schemas, access control, and lineage to master-data assets.

Data Quality & Governance

- Define, implement, and monitor data-quality rules (completeness, validity, uniqueness, conformity) with measurable DQ scoring at the domain level.
- Build reconciliation and duplicate-detection reporting so match accuracy and record counts are visible and defensible.
- Maintain audit trails, versioning, and change history on master records to meet governance and audit expectations.
- Partner with governance teams on business glossary, reference data, and stewardship policy alignment.

Integration & Downstream Enablement

- Integrate the MDM layer with upstream source systems (ERP, CRM, third-party data providers) and publish mastered data downstream via APIs, files, or streaming.
- Expose curated golden-record datasets to analytics, BI, and data-science consumers with documented contracts and SLAs.
- Handle real-time and batch integration patterns as the use case requires.

Delivery & Collaboration

- Work with solution architects and business stakeholders to translate data-management requirements into technical designs.
- Produce clear technical documentation — data models, mapping specifications, match-rule definitions, and runbooks.




- Participate in Agile ceremonies, code reviews, and CI/CD practices; support UAT and production hypercare.

REQUIRED SKILLS & EXPERIENCE

- Approximately 5 years of data engineering experience, with a substantial portion spent hands-on in Master Data Management.
- MDM Depth: Practical, build-level experience with MDM concepts — golden record creation, match and survivorship rules, data stewardship, hierarchy management, and reference data management.
- Reltio: Hands-on, production experience on the Reltio Connected Data Platform — entity and relationship modelling, L3 configuration, match and survivorship rule design, workflow and stewardship setup, and integration through Reltio APIs / Integration Hub.
- Databricks (Good to have): Hands-on development experience with Databricks, PySpark, Spark SQL, and Delta Lake in a production environment.
- SQL: Solid, advanced SQL — complex joins, window functions, query tuning, and working with large volumes.
- Python: Production-quality Python for data engineering, including modular, testable code.
- Data Modelling: Solid understanding of dimensional and master-data modelling, slowly changing dimensions, and source-to-target mapping.
- Cloud: Working experience on at least one major cloud platform (Azure or AWS) and its core data services.
- Experience profiling and remediating poor-quality data from multiple heterogeneous sources.
- Version control (Git) and familiarity with CI/CD for data pipelines.
- Clear communication skills — able to explain match logic and data-quality outcomes to non-technical stakeholders.

GOOD TO HAVE

- Additional MDM Platforms: Exposure to Informatica MDM / IDMC (MDM SaaS), Profisee, Semarchy xDM, Stibo STEP, SAP MDG, or IBM InfoSphere MDM alongside Reltio.
- Data Quality Tools: Informatica Data Quality (IDQ), Ataccama ONE, Great Expectations, or Soda.
- Domain Exposure: Life Sciences / Pharma master data — HCP/HCO mastering, Veeva, IQVIA or Symphony reference data, and working within regulated, audit-ready environments.

📌 Reltio MDM Developer (Bengaluru)
🏢 Quantzig
📍 Bengaluru