Summary of Position:
- This role is responsible for the Data Engineering of Master Data Management (MDM) data within Alcon's AWS data lake environment. Working closely with business and data governance stakeholders, the Data Engineer will design, build, and optimize scalable data pipelines that ingest, cleanse, transform, and provision trusted master data — with a primary focus on Customer Master data and Product Information Management (PIM) data domains — in alignment with Alcon's Data and Analytics strategy, standards, and governance practices.
- Knowledge of Customer Master data and Product Information Management (PIM) data domains is highly preferred.
Key Responsibilities:
- Design, build, and maintain scalable data pipelines to ingest, cleanse, transform, and curate Master Data Management (MDM) data — especially Customer Master and Product Information Management (PIM) data — within the AWS data lake.
- Adopt AWS data lake and related data services to implement end-to-end MDM data solutions across ingestion, integration, curation, and consumption.
- Deliver business solutions on Alcon's analytics platform through end-to-end implementation that includes data security, governance, cataloging, preparation, automated testing, and data quality metrics.
- Build data pipelines using Python, PySpark, and Spark SQL, integrating master data from a variety of heterogeneous source systems.
- Collaborate with data governance teams and information stewards to support data quality, stewardship, matching/survivorship, and lifecycle management for Customer Master and PIM data.
- Automate, optimize, migrate, and enhance existing MDM data solutions and pipelines.
- Perform data modeling, data analysis, and provide insights that support trusted customer and product master data for downstream analytics use cases.
- Participate in backlog grooming, sprint planning, and effort estimation as part of an agile DevOps team.
- Analyze and manage master data within Master Data Management (MDM) platforms such as Reltio, including customer, product, supplier, and reference data domains.
- Configure, maintain, and analyze Product Information Management (PIM) solutions such as Stibo to ensure high-quality product content, digital assets, classifications, attributes, and product lifecycle data.
- Utilize data cataloging and metadata management tools to document, classify, govern, and improve discoverability of enterprise data assets, business glossaries, lineage, and data quality metrics.
Key Requirements/Minimum Qualifications:
- Education: B.Tech / B.E / M.Tech / MCA or bachelor's/master's degree in Computer Science, Data Science, or a related quantitative field (or equivalent applicable experience).
- 6 to 10 years of experience in Big Data / data lake solutions in an AWS cloud environment.
- 3+ years of experience writing code in the Spark engine using Python, Scala, or Java.
- Hands-on experience building and optimizing data pipelines for data ingestion, preparation, curation, provisioning, automated testing, and quality checks.
- Good working knowledge of AWS services such as Glue, EMR, S3, SNS, SQS, Athena, Redshift, Lambda, and Step Functions.
- Good experience in RDBMS — MS SQL, Oracle, Postgres, etc.; strong working knowledge of SQL is a must.
- Experience performing data modeling, data analysis, and deriving insights using various tools.
- Experience working as part of agile teams using collaboration tools such as Jira and Confluence.
- Ability to work with the business to capture, groom, prioritize, plan, and demo User Stories.
- Familiarity with visualization and reporting tools such as Tableau or AWS Quick.
Highly Preferred
- Knowledge of and hands-on experience with Master Data Management (MDM) concepts, tooling, and processes.
- Working knowledge of Customer Master data domains (customer records, hierarchies, matching, de-duplication, and survivorship).
- Working knowledge of Product Information Management (PIM) data domains (product attributes, catalogs, taxonomies, and enrichment).
- Understanding of data governance, data quality, and stewardship practices as applied to master data.
Interpersonal Skills & Characteristics
- Strong collaboration skills for effective communication across multiple teams and stakeholders, both internal and external.
- Data-savvy individual with hands-on experience preparing, analyzing, and deriving insights from data.
- Confident, energetic self-starter with strong problem-solving ability and high learning agility.
- Demonstrated commitment to high standards of ethics, regulatory compliance, customer service, and business integrity.
Work hours: 1 PM to 10 PM IST
Relocation assistance: Yes
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