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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, metro location, and popular Data Engineer title increase candidate density despite seniority and niche skills.
Technical data engineering skills (Spark, Databricks, cloud, Python) are broadly transferable across industries.
Explicit 7+ years plus mandatory Spark/Databricks, cloud, and leadership requirements make shortlisting strict.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and implement cloud-based data and analytics platforms and pipelines to acquire, cleanse, transform, and publish data from various sources.
Ensure technical solutions align with architecture blueprints and provide data fit for business use, working closely with data asset managers and architects.
Lead technical design discussions and drive solution decisions in cross-functional and global teams as a hands-on technical lead.
Minimum Requirements
Bachelor’s degree in Computer Science, Engineering, Information Technology, or related technical field, or equivalent experience.
7+ years of experience in data engineering with hands-on expertise in Big Data solutions using Spark and Databricks.
At least 3 years of experience with cloud technologies such as Azure Cloud or Google Cloud, including software engineering and API development.
Proven experience designing and developing end-to-end data solutions including architecture, coding, testing, deployment, and production support.
Ideal Candidate Profile
Strong expertise in modern data platform architectures including Data Warehousing, Lakehouse, Data Mesh, and Data Quality.
Proficient in Python (PySpark), SQL, and familiar with LLM orchestration frameworks and context engineering for data-facing agents.
Experienced in CI/CD pipelines, API integrations, Agile and DevOps environments, and capable of leading technical design and collaboration across global teams.