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Tier-1 brand, mid-level generalist data engineer with broad Azure/Databricks/Spark skillset increases competition.
Requires specific Azure/Databricks and enterprise data governance tools, limiting cross-industry portability.
Explicit 4+ years requirement plus mandatory Azure/Databricks/Spark/Python and enterprise tooling raises filtering strictness.
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Develop, integrate, and deliver reusable data solutions within Haleon’s Enterprise Data & Analytics Platform using latest data platform technologies.
Own project delivery from development through to production, collaborating with internal teams and third-party providers to meet quality and timeline goals.
Engage in platform expansion and governance, maintain risk and issue logs, and stay updated on industry analytics advancements to inform technology choices.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or equivalent experience.
Minimum 4 years of data engineering experience with proficiency in Python, SQL, Scala and Azure data analytics tools like Databricks, Data Factory, SQL DW.
Experience with enterprise data tools such as Ataccama, Talend, Collibra, Snowflake, and StreamSets; and agile delivery frameworks like SAFe, Jira, Azure DevOps.
Work Experience Required: Minimum 4 years in relevant data engineering roles. Notice period: Not explicitly mentioned in the JD.
Proven track record in applying big data processing technologies such as Spark, Databricks, and Delta Lake to solve business data science and machine learning problems.
Experienced in delivering enterprise-scale analytics solutions, translating business value of analytics projects, and scaling MVPs to production.
Communicates complex technical concepts effectively to non-technical stakeholders and collaborates well across IT functions and external technology providers.