Senior Technical Consultant, Data
New
A
AHEADData & Analytics
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8–12+ years
- Required Skills
- PythonSQLCloud ComputingETLSnowflakeData engineeringCI/CDData modelingDatabricks
Requirements
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Mathematics, Data Science, or related technical discipline, or equivalent professional experience.
- Typically 8–12+ years of professional technical experience delivering enterprise data, analytics, AI, cloud, or digital transformation solutions.
- Demonstrated consulting or professional services experience working directly with clients and independently owning complex technical workstreams.
- Strong hands-on experience in modern data engineering and architecture, including data modeling, SQL, Python, ETL/ELT, orchestration, and APIs.
- Experience with modern data platforms such as Snowflake, Databricks, or Microsoft Fabric.
- Experience working in AWS, Azure, or Google Cloud Platform environments.
- Proficiency in software/data engineering lifecycle practices including Git, CI/CD, automated testing, and observability.
- Strong ability to analyze complex source data, troubleshoot issues, and communicate findings to stakeholders.
- Demonstrated ability to guide other engineers and review technical work.
Responsibilities
- Own and lead complex technical workstreams or major solution components from discovery and design through build, test, deployment, and stabilization.
- Design, build, operationalize, secure, monitor, and optimize modern data solutions, including ingestion, transformation, orchestration, and integrations.
- Develop robust automated pipelines for structured and unstructured data using batch, streaming, and cloud-native patterns.
- Review code, designs, data models, and engineering deliverables for quality, consistency, and alignment with architecture standards.
- Operate as a consultant to understand client problems, business context, and constraints before recommending or implementing solutions.
- Contribute reusable engineering assets, implementation patterns, and standards to the internal Data Practice.
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