Divya Jennifer Jayaraj

I don’t think companies need analysts who “drive decisions,” I think they need analysts who stop the stupid ones. Not always. Not in every situation. But often. Most of the messes I’ve seen didn’t come from bad data, they came from overconfident timelines, half-baked assumptions and a room full of smart people nodding because they thought everyone had done their homework. Analysts see the leaps in logic and the numbers that don't quite add up, and we know no one really wants to discuss them... It's on us to ask the questions everyone is avoiding. When you bring a well-thought-out argument based on data and speak up, you'd be surprised how many people appreciate you giving voice to the things they were hesitant to say. So be bold, but bring data.

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Tiger Analytics is known for asking some solid, real-world Data Engineering questions during interviews. If you're preparing, here’s a quick snapshot of what they typically expect 👇 💰 Role-wise Compensation (Approx.) Data Engineer: ₹15–20 LPA Senior Consultant – Data Engineering: ₹25–30 LPA 📌 Commonly asked technical questions: 1️⃣ How would you build a highly scalable ingestion pipeline for hourly multi-TB parquet files landing in ADLS using ADF + Databricks? Consider partitioning, pruning, and metadata performance tuning. 2️⃣ Explain the internals of OPTIMIZE and ZORDER in Delta Lake. Also discuss the scenarios where running them may not be beneficial. 3️⃣ Write a SQL query to find customers who purchased in 3 or more back-to-back months. 4️⃣ What’s the performance difference between using Delta Lake CDF and MERGE? When is one a better choice over the other? 5️⃣ How does Spark’s Tungsten engine speed up execution? Talk about off-heap memory and whole-stage code generation. 6️⃣ A Databricks job fails intermittently due to data skew. How do you diagnose it and what steps would you take to mitigate it? 7️⃣ What are the best practices for building resilient ADF pipelines? Think retries, monitoring, alerting, and custom logging. 8️⃣ How would you design a watermark strategy in Spark Structured Streaming when events arrive late or out of order? 9️⃣ Delta Live Tables vs traditional Spark notebooks: When should each be used for production-grade ETL? 🔟 Explain SCD Types 1, 2, and 3. How would you efficiently implement Type 2 using Window functions + MERGE? 1️⃣1️⃣ How do you build enterprise data lineage with Purview integrated across ADF and Databricks? 1️⃣2️⃣ Compare Databricks SQL Warehouse with Azure Synapse Dedicated SQL Pool for analytics workloads. 1️⃣3️⃣ Describe a real scenario where you applied data quality checks (schema, nulls, referential integrity) and how you set up automated alerts. 1️⃣4️⃣ Share an experience where you reduced Databricks costs using autoscaling, spot nodes, or better query optimization. 1️⃣5️⃣ Design a near real-time Kafka → Spark Streaming → Delta pipeline. How do you guarantee exactly-once processing and handle replay logic? 👉 If you found this helpful, hit LIKE 👍 and RESHARE 🔁 so more people can benefit from it! 📍 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗱𝗮𝗶𝗹𝘆 𝗦𝗤𝗟, 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗝𝗼𝗯 𝘂𝗽𝗱𝗮𝘁𝗲𝘀: 👉 https://lnkd.in/dhYpfrS7 𝗜 𝗵𝗮𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗻𝗲𝗿𝘀/𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀. 👉𝗚𝗿𝗮𝗯 𝗶𝘁 𝗵𝗲𝗿𝗲(𝟭𝟬𝟬% 𝗥𝗲𝗳𝘂𝗻𝗱): https://lnkd.in/giHUMNx9

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🎥 Just dropped a quick walkthrough of my latest Power BI dashboard! This project dives into retail product sales and customer retention, powered by data from SQL Server and visualized with dynamic DAX-driven insights. 🔍 Inside the dashboard: ✅ $29M+ in revenue analyzed ✅ Customer behavior segmented by age & order patterns ✅ Product-level profit and performance ✅ Strategic recommendations based on real metrics 📊 If you're into data storytelling, visual analytics, or just love seeing dashboards that do more than look pretty — this one's for you! 👉 I'd love your thoughts or feedback! 👇 #PowerBI #DataAnalytics #SQL #DataVisualization #DashboardDesign #CustomerInsights #DAX #BusinessIntelligence #AnalyticsInAction

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🚀 Want to double your chances of cracking a Data Analytics interview? From the Interview Room: Cognizant Data Analytics Questions You Must Know I’ve gathered real Cognizant interview questions to help you prepare smarter. These are not hypothetical — they’re exactly what candidates faced. When you prepare with real questions, you: ✅ Focus on the right topics instead of wasting time on irrelevant ones ✅ Learn how to structure your answers for maximum impact ✅ Walk into the interview without surprises, ready for anything I’ve compiled these questions into a well-organized, easy-to-use PDF so you can start practicing today. Whether you’re a fresher or an experienced professional, this resource will sharpen your preparation and give you an edge over other candidates. PS : If you want to learn data analytics from me ( paid ) then you can join this group : https://lnkd.in/g9Y2dP8F 📄 PDF credit goes to respective owner 💬 Comment "Cognizant" if you want it Follow Ajay Y. for more resources ##dataanalytics #dataanalyst #dataanalysis #sql #python #excel #tableau #powerbi #businessintelligence #analytics #datascience #datascientist #machinelearning #interviewquestions #jobsearch #career #careertips #careergrowth #jobinterview #jobinterviewtips #interviewprep #cognizant #mockinterview #bigdata #datadriven

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💡 Real Healthcare Databricks Interview Questions & My Answers (8 Yrs Experience) Sharing 5 real-world scenario-based questions I faced as a Senior Data Engineer in Healthcare 👇 These reflect real production challenges from handling PHI to building scalable Delta pipelines. 1️⃣ How do you ensure HIPAA compliance while processing data in Databricks? 🩺 Answer: “In healthcare projects, I always enforce PHI protection using column-level encryption and access control via Unity Catalog. All PII fields are tokenized before landing in Delta tables, and audit logs are maintained using Databricks audit logs + Azure Monitor. We also integrate with Azure Key Vault for credential rotation and secure secrets management.” 2️⃣ How do you optimize Delta tables storing EHR/Claims data with billions of rows? 📊 Answer: “I apply ZORDER on high-cardinality columns like patient_id or claim_id for faster lookups. Partitioning is done by encounter_date or claim_year to improve pruning. I’ve also used OPTIMIZE with VACPUM scheduling and Delta caching for repeated analytical workloads — achieving a 45% boost in query performance.” 3️⃣ How do you handle incremental updates from HL7/FHIR streaming feeds? ⚙️ Answer: “I built a streaming ingestion using Auto Loader + Structured Streaming. Each micro-batch merges data into the Delta table using MERGE INTO with CDC logic. Watermarking ensures late-arriving updates are captured without duplication. Schema drift is handled via mergeSchema = true since FHIR resources often evolve. 4️⃣ How do you manage schema evolution across Bronze–Silver–Gold layers? 🏗️ Answer: “In our Medallion Architecture, we validate schema consistency at each layer. Bronze ingests raw JSON, Silver applies type casting + deduplication, and Gold provides curated analytics. To manage evolving schemas, I version schemas in Delta logs and maintain a schema registry ensuring backward compatibility.” 5️⃣ How do you build a unified pipeline for batch claims data and real-time clinical events? 🚀 Answer: “I designed a hybrid ingestion framework in Databricks that handles both historical (batch) data and real-time (streaming) HL7 feeds. Batch loads initialize the Delta tables, while streaming updates are applied using foreachBatch. The architecture supports both CDC and append-only streams for lab results, vitals, and patient admissions.” These real-world approaches have helped me build secure, compliant, and high-performing healthcare data platforms using Databricks, Azure, and Delta Lake. #Databricks #HealthcareDataEngineering #Azure #DeltaLake #MedallionArchitecture #CDC #HL7 #FHIR #DataEngineering #InterviewPreparation Kforce Inc IntraEdge Cardinal Health UnitedHealth Group Lakshya Technologies

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