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Data Architect/ Technical Architect

Tata Consultancy Services · Bengaluru, Karnataka, India

12–20 yrs experiencefull_timePosted 2w ago
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Job description

I hope you're doing well! I came across your profile and was impressed by your extensive experience as an/a (mention role/skill). We have a similar opportunity to join our dynamic team at TCS **Role: Data Architect/ Technical Architect** **Location - PAN INDIA** **Experience - 8 to 15 years** **Mode: Full time** Job description Design of data lakes, lakehouses, data warehouses, and real-time streaming platforms, ETL/ELT & Pipeline Development, Data Pipelines, Regulations and Compliances, Model building processes, Data Governance, orchestration tools like Airflow and other workflow platforms, Azure Data Factory pipelines, Databricks. modern data stacks (Snowflake, Databricks, Apache Spark, Kafka). Programming skills like SQL, Python, and data transformation logic Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field. 8-12 years of experience in data architecture, including 3+ years in cloud-native environments. Responsibility of / Expectations from the Role Design and implement enterprise-wide data architecture strategies in the cloud (AWS, Azure, GCP, or multi-cloud). Architect data lakes, lakehouses, data warehouses, and real-time streaming platforms. Define and enforce data modeling standards (dimensional, NoSQL, normalized) across the organization. Collaborate with data engineers, analysts, scientists, and DevOps to translate business needs into scalable data solutions. Design data ingestion pipelines (batch, stream) using modern ETL/ELT frameworks. Ensure high levels of data quality, security, lineage, and governance. Evaluate and recommend cloud-native services and tools based on evolving needs. Create architecture documentation, data flow diagrams, and reference implementations. Drive data modernization efforts and lead legacy-to-cloud migration initiatives. Stay up to date with emerging trends in cloud data platforms, AI/ML workloads, and data compliance regulations.