Reltio MDM Developer

Quantzig AI Solutions India Private Limited; Bengaluru; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

Quantzig AI Solutions India Private Limited in Bengaluru is seeking an hands-on Data Engineer specializing in Master Data Management to build and operate the data backbone of an analytics platform using Reltio and Databricks.

You will implement MDM pipelines, write production-grade Spark and SQL, and own data-quality rules end-to-end while collaborating with data stewards, architects, and client stakeholders to deliver a trusted single source of truth.

Qualifications

• 5+ years of data engineering with hands-on MDM experience.
• Deep expertise in MDM concepts, golden records, and survivorship rules.
• Practical Reltio knowledge: entity, relationships, rules, APIs.
• Strong SQL and Python for production data pipelines.
• Experience with Databricks and Delta Lake is preferred.

Responsibilities

• Build and maintain MDM pipelines across core domains to publish golden records.
• Design and tune match, survivorship, and merge logic.
• Develop ETL/ELT pipelines on Databricks using PySpark and Spark SQL.
• Ensure data quality and governance across multi-source data.
• Collaborate with data stewards, architects, and client stakeholders.
• Document data models, mappings, and runbooks; participate in CI/CD.

Skills

MDM engineering

Reltio

SQL

Python

Databricks

Data modeling

Cloud platforms

Git

CI/CD

Tools

Reltio Integration Hub

Delta Lake

Spark

Databricks Workflows

Job description

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: Strong, 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.