What do I know about Nasdaq?

Nasdaq is a global technology company that operates financial markets, provides market infrastructure, and delivers data and analytics solutions. It's primarily known for its electronic stock exchange and its role in listing many of the world's leading technology companies, but its scope extends to market technology, clearing, and investor services.

For data engineering, Nasdaq represents a fascinating and demanding environment due to the immense scale, velocity, and criticality of its data. Its core business generates vast amounts of real-time market data—order book changes, trades, quotes—often millions of events per second, requiring ultra-low latency ingestion and processing. This necessitates robust streaming architectures, typically leveraging systems like Apache Kafka for high-throughput messaging and Apache Spark for real-time analytics, complex event processing, and market surveillance. The regulatory environment (e.g., SEC, FINRA) imposes strict requirements for data integrity, auditability, and immediate reporting, making data quality and governance paramount.

Beyond real-time, there's a critical need for historical data storage and analysis. Petabytes of data must be reliably stored in data lakes (e.g., using Delta Lake on cloud storage) for regulatory compliance, market surveillance, backtesting, and product development. Data engineers at Nasdaq would build and maintain complex pipelines for data ingestion, transformation, quality assurance, and delivery of various data products and analytical insights. For example, calculating daily trading volumes or volume-weighted average prices (VWAP) requires processing large, time-series datasets efficiently, often involving techniques like window functions and efficient partitioning.

SELECT
    trade_date,
    symbol,
    SUM(quantity * price) / SUM(quantity) AS vwap
FROM
    daily_trades
WHERE
    trade_date = '2023-10-26'
GROUP BY
    trade_date, symbol;

In the interview, also mention how your experience with distributed systems, real-time data processing, or data governance aligns with the challenges of operating global financial markets.