Help & Supportcareers@gravityer.com
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Engineering Leader | 13+ Yrs in Data Engineering, Data Architecture & Cloud Analytics | AWS Certified
𝗜 𝗕𝗨𝗜𝗟𝗗 𝗦𝗖𝗔𝗟𝗔𝗕𝗟𝗘 𝗗𝗔𝗧𝗔 𝗣𝗟𝗔𝗧𝗙𝗢𝗥𝗠𝗦 𝗧𝗛𝗔𝗧 𝗧𝗥𝗔𝗡𝗦𝗙𝗢𝗥𝗠 𝗖𝗢𝗠𝗣𝗟𝗘𝗫 𝗗𝗔𝗧𝗔 𝗜𝗡𝗧𝗢 𝗕𝗨𝗦𝗜𝗡𝗘𝗦𝗦 𝗜𝗡𝗦𝗜𝗚𝗛𝗧𝗦. As a Lead Data Engineer at Barclays with 13+ years of experience, I specialize in Data Engineering, Cloud Data Platforms, Data Architecture, Big Data, and Analytics. My career has evolved from ETL development to leading enterprise-scale data initiatives, helping organizations build reliable, scalable, and high-performing data ecosystems. 𝗘𝗫𝗣𝗘𝗥𝗧𝗜𝗦𝗘 I bring deep expertise in AWS, Apache Spark, Airflow, Kafka, Flink, Snowflake, Databricks, dbt, Python, Cassandra, Neo4j, Oracle, Delta Lake, Apache Hudi, and Apache Iceberg. My focus is on designing modern data platforms, optimizing data pipelines, strengthening data quality, and enabling data-driven decision-making at scale. 𝗞𝗘𝗬 𝗜𝗠𝗣𝗔𝗖𝗧 • Spearheaded enterprise data warehousing, analytics, and information delivery roadmaps. • Established a comprehensive DataOps framework for Data and Analytics teams. • Architected data quality, automated validation, and testing frameworks using BDD and TDD methodologies. • Built internal collaboration platforms and automation solutions that improved delivery efficiency. • Developed a sample data generator tool that reduced ETL testing time by 30%. • Delivered cloud and platform optimization initiatives focused on operational efficiency and cost reduction. 𝗕𝗘𝗬𝗢𝗡𝗗 𝗧𝗛𝗘 𝗥𝗢𝗟𝗘 I actively contribute to the open-source data community, write about modern data engineering practices, and explore emerging technologies across AI, Machine Learning, and Data Infrastructure. 𝗖𝗘𝗥𝗧𝗜𝗙𝗜𝗖
Indira Gandhi National Open University
Master of Arts - MA, Psychology
January 1, 2020 – January 1, 2022
RN Shetty Institute of Technology
Bachelor's degree, Computer Science
January 1, 2008 – January 1, 2012
Barclays UK
Lead Data Engineer
November 1, 2024 – Present
Bengaluru, Karnataka, India · On-site
Tesco Bengaluru
Data Engineering Lead
September 1, 2021 – November 1, 2024
Tesco Bengaluru
Data Engineer
October 1, 2016 – August 1, 2021
ANZ
Technical Analyst
April 1, 2015 – September 1, 2016
Bengaluru Area, India
Accenture in India
Software Engineering Analyst
February 1, 2013 – April 1, 2015
Bangaon Area, India
Enverus
Intern
November 1, 2012 – January 1, 2013
Bangalore
AI-ready lakehouse with governed RAG
February 1, 2026 – Present
Designed and built an open-table-format lakehouse (Apache Iceberg on S3) that serves both BI and GenAI from one governed source of truth. Ingestion via Airbyte and Kafka feeds a medallion architecture; a downstream embedding pipeline chunks and vectorises curated Gold-layer data into a vector store for retrieval-augmented generation. Data contracts, Great Expectations validation, Spline lineage and column-level PII masking are enforced before any document reaches an embedding model — so the AI layer inherits trusted, governed data by default. Stack: Apache Iceberg, S3, Glue, Airbyte, Kafka, dbt, Airflow, Great Expectations, Spline, a vector store (pgvector / OpenSearch), embeddings + LLM.
Real-time streaming lakehouse for ML features and live analytics
January 1, 2026 – Present
Built a near-real-time streaming lakehouse that powers live dashboards and online ML features from the same pipeline. Application events and database CDC stream through Kafka into Apache Flink for windowed aggregations and enrichment, then land as upserts in Apache Hudi on S3. Curated outputs feed Apache Superset for sub-minute operational dashboards and a feature store for low-latency model serving — collapsing the usual gap between analytics and ML data. Stack: Kafka, Apache Flink, Apache Hudi, S3, Spark, Apache Superset, feature store, Python.
FinOps / cost-optimized data platform
April 1, 2025 – November 1, 2025
Led a cost-optimization initiative across cloud data pipelines, introducing usage observability, right-sized compute, partition/file compaction on the lakehouse, and query optimization on Athena/Glue. Established a FinOps feedback loop with per-pipeline cost attribution so teams could see and own their spend. Pair it with a hard number — your resume already implies measurable savings, so quantify it (e.g. "reduced pipeline operating cost by X%"). Stack: AWS (Glue, Athena, S3, Lambda, CloudFormation), Iceberg/Hudi compaction, cost dashboards, Python.
Data reliability platform — contracts, quality gates and observability
February 1, 2024 – October 1, 2025
Designed a data reliability framework that treats data quality as a CI/CD concern. Producer-consumer data contracts define schema and freshness SLAs; every Airflow/dbt run passes through automated quality gates (Great Expectations, Soda, Deequ) that block bad data from propagating. Spline lineage plus anomaly alerting give downstream teams full observability — cutting reactive pipeline firefighting and raising trust in the data feeding analytics and AI. Stack: Airflow, dbt, Great Expectations, Soda SQL, Deequ, Spline, data contracts, CI/CD.
Microsoft Certified: Azure Fundamentals
Microsoft
June 25, 2026 – Present
IBM Certified Data Engineer - Big Data
IBM
June 25, 2026 – Present
Astronomer Certification for Apache Airflow Fundamentals
Astronomer
June 25, 2026 – Present
IBM Certified Solution Developer - InfoSphere DataStage v9.1
IBM
June 25, 2026 – Present
Aws Certified Data Engineer - Associate
Amazon Web Services (AWS)
June 25, 2026 – Present
Data Lake - Databricks
Databricks
June 25, 2026 – Present
Developer Associate
Amazon Web Services (AWS)
June 25, 2026 – Present
Machine Learning
Coursera
June 25, 2026 – Present
Cultural Fit Analysis
The candidate's experience spans multiple large enterprises (Barclays, Tesco, ANZ, Accenture) and includes diverse projects from AI-ready lakehouses to FinOps and data reliability platforms. This breadth of experience and exposure to different problem domains suggests adaptability and a willingness to tackle varied challenges. The focus on establishing frameworks and driving best practices indicates a proactive and improvement-oriented mindset. However, the target role is 'Data Analyst' while the experience is heavily skewed towards 'Data Engineer' and 'Lead Data Engineer'. While the underlying data skills are relevant, the shift in focus from building/leading data platforms to primarily analyzing data might require adjustment. The projects demonstrate a strong engineering and architectural bent, which might be overqualified or misaligned if the Data Analyst role is purely consumption-focused.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a strong focus on data reliability, cost optimization, and establishing frameworks (DataOps, data quality), suggesting an operational mindset and an ability to drive best practices. The experience in leading initiatives and collaborating across teams indicates strong leadership and teamwork skills. The pursuit of a Master's in Psychology suggests an interest in human behavior and potentially strong analytical and problem-solving skills, which are beneficial for understanding business requirements and user needs.