At my previous company, the analytics team relied on a legacy batch pipeline that caused daily delays.

I needed to propose a streaming solution using Kafka and Flink to reduce latency.

I built a proof‑of‑concept, quantified performance gains, and presented a cost‑benefit analysis to the data ops lead and product manager.

Stakeholders approved the project, leading to a 70% reduction in data latency and a 20% increase in user engagement metrics.

We were migrating a legacy data warehouse to Snowflake with a fixed go‑live date.

My role was to ensure data quality checks were completed on time.

I underestimated the volume of custom transformations, causing a bottleneck. I escalated early, re‑prioritized tasks, and added a temporary resource.

We launched two weeks later, but the extra QA prevented data inconsistencies. The experience taught me to build buffer time and improve scope estimation.

A new graduate joined our data engineering team and struggled with Airflow DAG design.

I needed to bring them up to speed on modular DAG construction and testing.

I paired with them for two weeks, introduced unit‑testing frameworks, and created a reusable DAG template library.

Their first independent pipeline passed all tests on the first run, reducing onboarding time for future hires by 30%.

Our data lake suffered from schema drift causing downstream failures.

Decide whether to enforce strict schema validation at ingestion or handle drift downstream.

I introduced a schema‑registry‑backed validation layer using Apache Avro, coupled with automated alerts for mismatches.

Data reliability improved by 45%, and downstream teams reported fewer broken pipelines.

Need to report high‑value customers for quarterly business review.

Create a query that aggregates net revenue per customer, filters by date, and excludes returned orders.

Used CTEs to sum order amounts, joined with returns table, applied date filter, and ordered by revenue descending with LIMIT 5.

Returned a concise list of the top 5 customers, enabling the sales team to target upsell opportunities.

Designing a data warehouse for an e‑commerce analytics platform.

Select an optimal schema to balance query performance and storage efficiency.

Described star schema as denormalized with single‑level dimension tables for fast query performance; snowflake schema normalizes dimensions into multiple related tables, reducing redundancy but adding join complexity. Recommended star schema for ad‑hoc reporting and snowflake when dimensions are highly hierarchical and storage cost is a concern.

Chosen schema aligned with reporting latency requirements and storage constraints, improving dashboard response times by 25%.

Product team needed daily active user metrics over a rolling week.

Create a query that counts distinct users for each day based on the previous 7 days of events.

Used window functions: SELECT event_date, COUNT(DISTINCT user_id) OVER (PARTITION BY event_date ORDER BY event_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS rolling_active_users FROM (SELECT DATE(event_timestamp) AS event_date, user_id FROM events) sub;

Delivered a time‑series of rolling active users that fed directly into the product dashboard, enabling trend analysis.

Need to store high‑volume clickstream for analytics while keeping storage efficient.

Create a set of tables that capture sessions, pages, and events with proper foreign keys.

Proposed three tables: Sessions(session_id PK, user_id, start_time, end_time), Pages(page_id PK, url, title), Events(event_id PK, session_id FK, page_id FK, event_type, event_timestamp). Added indexes on session_id and page_id for aggregation.

Model allowed aggregations like page views per session with simple joins, supporting sub‑second query response in the reporting layer.

Our microservice architecture emitted JSON logs to a Kafka topic that needed near‑real‑time analytics.

Build a scalable pipeline that cleanses, enriches, and persists logs for downstream BI.

Implemented a Spark Structured Streaming job in Python that reads from Kafka, applies schema validation and enrichment (lookup from DynamoDB), writes to Delta Lake on S3 with checkpointing in S3, and registers the table in AWS Glue Catalog. Deployed the job on EMR Serverless for auto‑scaling.

Latency dropped to under 30 seconds, and data consumers accessed clean logs via Athena with sub‑second query latency.

Startup needed a data warehouse to support ad‑hoc analytics and growing data volume.

Choose between Snowflake (serverless) and Redshift (provisioned).

Compared cost model (pay‑per‑query vs reserved instances), elasticity (auto‑scale vs manual scaling), concurrency handling, ecosystem integration, and data sharing features. Recommended Snowflake for its instant scaling, zero‑maintenance, and per‑second billing, while noting Redshift’s lower cost at steady high utilization and tighter integration with AWS services.

Decision aligned with the startup’s growth trajectory, allowing cost‑effective scaling and faster time‑to‑insight.

Our organization required end‑to‑end visibility of data transformations for compliance.

Create lineage tracking across Dataflow jobs, BigQuery tables, and Cloud Storage buckets.

Instrumented each Dataflow pipeline with Cloud Data Catalog tags, emitted custom metadata events to Pub/Sub, and used Cloud Composer to orchestrate and log DAG runs. Developed a lineage UI using Looker Studio that queries the metadata tables in BigQuery, showing source‑to‑target mappings and timestamps.

Achieved 100% automated lineage capture, satisfying audit requirements and reducing manual documentation effort by 80%.

A nightly aggregation job on Databricks was exceeding its SLA due to excessive disk spill.

Identify root cause and optimize memory usage.

Checked Spark UI for task metrics, observed high shuffle read size and low executor memory. Increased executor memory, tuned spark.sql.shuffle.partitions, enabled adaptive query execution, and applied broadcast joins where appropriate. Also persisted intermediate DataFrames with appropriate storage levels to avoid recomputation.

Spill reduced by 90%, job runtime dropped from 45 minutes to 18 minutes, meeting SLA.