Preparing for a Data Engineer interview at Persistent requires a balanced approach. You must demonstrate strong hands-on coding capabilities while also proving that you can architect scalable, secure, and compliant cloud data solutions.

Technical Depth in Core Tools – You must show a deep, practical understanding of SQL, Python, and PySpark. The interviewers will evaluate your ability to write clean, optimized code and explain the underlying mechanics of distributed processing engines like Spark.

Cloud Architecture & Lakehouse Concepts – Be ready to discuss how you design modern data architectures. You should be comfortable explaining migrations from legacy systems to Delta Lake on Azure Databricks, Microsoft Fabric, or GCP BigQuery, showing that you understand how to balance cost, performance, and scalability.

Data Governance & Quality Mindset – At Persistent, data is treated as a highly governed asset. You will be evaluated on your ability to implement data quality frameworks, design robust error-handling mechanisms, and execute secure data lifecycle policies (such as archival and deletion).

Collaborative Problem Solving – You will often work closely with cross-functional teams, including data scientists, AI engineers, and business stakeholders. Interviewers look for candidates who can clearly articulate technical concepts to non-technical audiences and collaborate effectively in an Agile environment.

The interview process at Persistent is structured to thoroughly evaluate both your technical expertise and your cultural alignment with the company's values-driven, people-centric environment. The process typically moves at a steady pace, taking anywhere from two to four weeks from the initial application to the final offer.

You will encounter a mix of live coding assessments, system design discussions, and behavioral interviews. Persistent places a heavy emphasis on practical, real-world experience, so expect rounds that focus on your past projects, technical decision-making, and ability to handle client-facing scenarios.

The timeline above outlines the typical progression of the interview stages. Candidates should use this visual guide to pace their preparation, ensuring they focus heavily on core coding and platform-specific architecture in the early stages, before shifting focus to system integration, client scenarios, and cultural alignment as they approach the final rounds.

To succeed in the Persistent interview process, you must excel across several core competency areas. Below is a detailed breakdown of what interviewers look for in each key evaluation area.

SQL & Database Optimization

SQL is the foundation of data engineering at Persistent. You must demonstrate more than just basic querying skills; interviewers want to see that you can write highly performant queries that process large datasets efficiently.

Be ready to go over:

• Query Optimization – Understanding query execution plans, identifying bottlenecks, and using indexes, partitioning, and clustering keys effectively.
• Analytical SQL – Advanced window functions, recursive common table expressions (CTEs), and complex aggregations.
• Data Warehousing Design – Designing dimensional models, handling slowly changing dimensions (SCDs), and optimizing storage.
• Advanced concepts (less common) – Tuning query performance on columnar storage engines, managing transaction locks in relational databases, and optimizing cross-database queries.

Example questions or scenarios:

• "How would you optimize a query that is performing a slow join between a 100-million-row fact table and a 50,000-row dimension table?"
• "Design a schema to track historical changes in employee department assignments over time, ensuring query efficiency for historical reporting."

Python, PySpark & Cloud Lakehouses

For modern data pipelines, Persistent heavily utilizes Python and distributed frameworks like PySpark on Azure Databricks or GCP. You need to show that you understand how distributed computing works under the hood.

Be ready to go over:

• Spark Architecture – Driver and executor nodes, JVM memory management, shuffling, and lazy evaluation.
• DataFrame Operations – Writing clean PySpark code for data transformation, filtering, and aggregation.
• Delta Lake Features – ACID transactions, time travel, schema enforcement, and optimization commands like OPTIMIZE and Z-ORDER.
• Advanced concepts (less common) – Writing custom Spark User Defined Functions (UDFs) and understanding their performance impact, tuning Spark serialization, and managing cluster auto-scaling policies.

Example questions or scenarios:

• "Explain what happens during a Spark shuffle operation and how you can structure your code to minimize shuffling."
• "How would you design a pipeline to ingest streaming data into a Delta table while maintaining low latency and preventing the 'small file problem'?"

ETL/ELT Orchestration & dbt

Building robust pipelines requires reliable orchestration and transformation tools. Persistent looks for experience with modern tools like dbt, Matillion, and Azure Data Factory (ADF).

Be ready to go over:

• Pipeline Orchestration – Setting up triggers, managing dependencies, parameterization, and dynamic scheduling in ADF or Airflow.
• dbt Modeling – Creating modular models, managing DAGs (Directed Acyclic Graphs), and implementing automated tests.
• Error Handling & Monitoring – Designing automated alerting, logging execution metrics, and building self-healing pipelines.
• Advanced concepts (less common) – Writing custom dbt macros, configuring blue-green deployment strategies for pipelines, and managing state in incremental dbt runs.

Example questions or scenarios:

• "How do you implement an incremental load strategy in dbt, and how do you handle late-arriving data?"
• "Describe how you would design an automated alerting system in Azure Data Factory to notify the engineering team via Slack or Email when a pipeline fails."

Data Governance, Security & Compliance

With clients in highly regulated industries, Persistent prioritizes data governance. You must show that you can secure data and comply with strict privacy policies.

Be ready to go over:

• Access Control – Implementing column-level and row-level security, and managing catalogs using Unity Catalog.
• Data Lifecycle Management – Designing automated processes for data archival and compliant deletion (e.g., GDPR 'Right to be Forgotten') triggered by governance platforms like BigID.
• Data Quality Frameworks – Implementing automated data validation checks at ingestion and transformation stages.
• Advanced concepts (less common) – De-identifying and masking sensitive PII/PHI data in real-time streams, and managing encryption keys for data at rest and in transit.

Example questions or scenarios:

• "How do you ensure that downstream reporting dashboards do not expose sensitive PII data to unauthorized users?"
• "Explain how you would validate referential integrity when deleting customer records across multiple relational databases and data lakes."