Experience: 10+ years of hands-on data engineering experience, with at least 3 years focused on the Databricks/Spark Ecosystem Databricks Expertise: Deep, hands-on expertise with the Databricks Lakehouse Platform, including Delta Lake, Structured Streaming, Lakeflow Declarative Pipelines (formerly Delta Live Tables), Databricks SQL, and cluster/serverless configuration and optimization. Business Semantics & Governance: Hands-on experience with Unity Catalog Business Semantics, including Metric Views (measures, dimensions, materialization), Domains, and Pages/Glossary to define governed, reusable KPIs once and serve them consistently across SQL, BI, and AI agents. Agentic & Conversational Analytics: Working knowledge/exposure of AI/BI Genie (Genie Spaces, Genie Ontology, trusted assets), Genie Code for agentic pipeline/SQL development, Databricks One for business-user consumption, and Mosaic AI for building and serving AI/ML models and agents. Programming Mastery: Expert-level proficiency in Python and PySpark. Advanced SQL skills are essential. Data Warehousing Concepts: Strong understanding of data modeling principles, including dimensional modeling (Kimball), data warehousing concepts, and ETL/ELT design patterns. Cloud Proficiency: Proven experience working with a major cloud provider (Azure, AWS, or GCP), particularly with data storage S3 and related services. Software Engineering Mindset: Experience with software engineering best practices, including version control (Git), code reviews, testing, and CI/CD. Certification: Databricks
Experience: 10+ years of hands-on data engineering experience, with at least 3 years focused on the Databricks/Spark
Ecosystem
Databricks Expertise: Deep, hands-on expertise with the Databricks Lakehouse Platform, including Delta Lake,
Structured Streaming, Lakeflow Declarative Pipelines (formerly Delta Live Tables), Databricks SQL, and cluster/serverless configuration and optimization.
Business Semantics & Governance: Hands-on experience with Unity Catalog Business Semantics, including Metric Views (measures, dimensions, materialization), Domains, and Pages/Glossary to define governed, reusable KPIs once and serve them consistently across SQL, BI, and AI agents.
Agentic & Conversational Analytics: Working knowledge/exposure of AI/BI Genie (Genie Spaces, Genie Ontology, trusted assets), Genie Code for agentic pipeline/SQL development, Databricks One for business-user consumption, and Mosaic AI for building and serving AI/ML models and agents.
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Programming Mastery: Expert-level proficiency in Python and PySpark. Advanced SQL skills are essential.
Data Warehousing Concepts: Strong understanding of data modeling principles, including dimensional modeling
(Kimball), data warehousing concepts, and ETL/ELT design patterns.
Cloud Proficiency: Proven experience working with a major cloud provider (Azure, AWS, or GCP), particularly with
data storage S3 and related services.
Software Engineering Mindset: Experience with software engineering best practices, including version control (Git),
code reviews, testing, and CI/CD.
Certification: Databricks
Roles & Responsibilities
Data Pipeline Development: Design, code, and deploy robust and scalable batch and streaming data pipelines using PySpark, Spark SQL, and Lakeflow (Lakeflow Connect for ingestion and Lakeflow Declarative Pipelines) to ingest data from sources such as Point-of-Sale (POS), e-commerce platforms, loyalty systems, and marketing clouds. Data Modeling & Transformation: Implement complex data transformations and business logic within the Medallion architecture (Bronze, Silver, Gold layers). Build and optimize the final "Gold" dimension tables that will serve as the single source of truth. Define governed business KPIs on top of the Gold layer using Unity Catalog Metric Views (business semantics) so metrics are computed once and reused consistently across BI, SQL, and Genie. Data Quality: Implement data quality frameworks and cleansing routines to ensure the accuracy and trustworthiness of the Customer 360 data. Performance Optimization: Proactively monitor, debug, and tune Databricks jobs and Spark clusters for performance and cost-efficiency. Implement best practices for partitioning, caching, and data layout in Delta Lake. Infrastructure as Code (IaC) & CI/CD: Work with DevOps teams to manage Databricks environments, clusters, and job deployments using tools like Terraform and AWS DevOps/GitHub Actions. Champion and implement CI/CD best practices for data pipelines. Data Governance & Security: Implement data governance features within Databricks Unity Catalog, including data lineage tracking, fine-grained access controls (row/column-level security and ABAC policies), tags, and data masking to ensure compliance and security across BI and AI/agent workloads. C ollaboration: Partner closely with Functional Consultants, Data Scientists, and Analytics Engineers to understand their data requirements and deliver well-structured, consumption-ready datasets. Semantic Modeling & Business Semantics: Build and govern Unity Catalog Business Semantics, authoring Metric Views (measures, dimensions, joins, synonyms, materialization), Domains, Pages, and Glossary terms, and certifying trusted assets so every dashboard, SQL query, notebook, and AI agent works from the same governed definitions. Genie & Agentic Analytics Enablement: Configure and curate AI/BI Genie Spaces and the Genie Ontology (instructions, trusted assets, example queries, synonyms) to deliver accurate natural-language, conversational analytics for business users, and enable consumption through AI/BI Dashboards and Databricks One.
Data Pipeline Development: Design, code, and deploy robust and scalable batch and streaming data pipelines
using PySpark, Spark SQL, and Lakeflow (Lakeflow Connect for ingestion and Lakeflow Declarative Pipelines) to ingest data from sources such as Point-of-Sale (POS), e-commerce platforms, loyalty systems, and marketing clouds.
Data Modeling & Transformation: Implement complex data transformations and business logic within the Medallion
architecture (Bronze, Silver, Gold layers). Build and optimize the final "Gold" dimension tables that will
serve as the single source of truth. Define governed business KPIs on top of the Gold layer using Unity Catalog Metric Views (business semantics) so metrics are computed once and reused consistently across BI, SQL, and Genie.
Data Quality: Implement data quality frameworks and cleansing routines to ensure the accuracy and trustworthiness
of the Customer 360 data.
Performance Optimization: Proactively monitor, debug, and tune Databricks jobs and Spark clusters for performance
and cost-efficiency. Implement best practices for partitioning, caching, and data layout in Delta Lake.
Infrastructure as Code (IaC) & CI/CD: Work with DevOps teams to manage Databricks environments, clusters, and
job deployments using tools like Terraform and AWS DevOps/GitHub Actions. Champion and implement CI/CD best
practices for data pipelines.
Data Governance & Security: Implement data governance features within Databricks Unity Catalog, including
data lineage tracking, fine-grained access controls (row/column-level security and ABAC policies), tags, and data masking to ensure compliance and security across BI and AI/agent workloads.
C ollaboration: Partner closely with Functional Consultants, Data Scientists, and Analytics Engineers to understand
their data requirements and deliver well-structured, consumption-ready datasets.
Semantic Modeling & Business Semantics: Build and govern Unity Catalog Business Semantics, authoring Metric Views (measures, dimensions, joins, synonyms, materialization), Domains, Pages, and Glossary terms, and certifying trusted assets so every dashboard, SQL query, notebook, and AI agent works from the same governed definitions.
Genie & Agentic Analytics Enablement: Configure and curate AI/BI Genie Spaces and the Genie Ontology (instructions, trusted assets, example queries, synonyms) to deliver accurate natural-language, conversational analytics for business users, and enable consumption through AI/BI Dashboards and Databricks One.
TCS Employee Benefits Summary
Discretionary Annual Incentive. Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans. Family Support: Maternal & Parental Leaves. Insurance Options: Aut& Home Insurance, Identity Theft Protection. Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement. Time Off: Vacation, Time Off, Sick Leave & Holidays. Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
Family Support: Maternal & Parental Leaves.
Insurance Options: Aut& Home Insurance, Identity Theft Protection.
Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
Time Off: Vacation, Time Off, Sick Leave & Holidays.
