Data Engineering & Analytics Infrastructure Topics
Data pipeline design, ETL/ELT processes, streaming architectures, data warehousing infrastructure, analytics platform design, and real-time data processing. Covers event-driven systems, batch and streaming trade-offs, data quality and governance at scale, schema design for analytics, and infrastructure for big data processing. Distinct from Data Science & Analytics (which focuses on statistical analysis and insights) and from Cloud & Infrastructure (platform-focused rather than data-flow focused).
Data Quality and Governance
Covers the principles, frameworks, practices, and tooling used to ensure data is accurate, complete, timely, and trustworthy across systems and pipelines. Key areas include data quality checks and monitoring such as nullness and type checks, freshness and timeliness validation, referential integrity, deduplication, outlier detection, reconciliation, and automated alerting. Includes design of service level agreements for data freshness and accuracy, data lineage and impact analysis, metadata and catalog management, data classification, access controls, and compliance policies. Encompasses operational reliability of data systems including failure handling, recovery time objectives, backup and disaster recovery strategies, observability and incident response for data anomalies. Also covers domain and system specific considerations such as customer relationship management and sales systems: common causes of data problems, prevention strategies like input validation rules, canonicalization, deduplication and training, and business impact on forecasting and operations. Candidates may be evaluated on designing end to end data quality programs, selecting metrics and tooling, defining roles and stewardship, and implementing automated pipelines and governance controls.
Data Transformation and Preparation
Focuses on the technical skills and judgement required to connect to data sources, clean and shape data, and prepare datasets for analysis and visualization. Includes identifying necessary transformations such as calculations, aggregations, filtering, joins, and type conversions; deciding whether to perform transformations in the business intelligence tool or in the data warehouse or database layer; designing efficient data models and extract transform load workflows; ensuring data quality, lineage, and freshness; applying performance optimization techniques such as incremental refresh and pushdown processing; and familiarity with tools and features such as Power BI Power Query, Tableau data preparation capabilities, and structured query language for database level transformations. Also covers documentation, reproducibility, and testing of data preparation pipelines.
Data Pipeline Scalability and Performance
Design data pipelines that meet throughput and latency targets at large scale. Topics include capacity planning, partitioning and sharding strategies, parallelism and concurrency, batching and windowing trade offs, network and I O bottlenecks, replication and load balancing, resource isolation, autoscaling patterns, and techniques for maintaining performance as data volume grows by orders of magnitude. Include approaches for benchmarking, backpressure management, cost versus performance trade offs, and strategies to avoid hot spots.
Data Processing and Transformation
Focuses on algorithmic and engineering approaches to transform and clean data at scale. Includes deduplication strategies, parsing and normalizing unstructured or semi structured data, handling missing or inconsistent values, incremental and chunked processing for large datasets, batch versus streaming trade offs, state management, efficient memory and compute usage, idempotency and error handling, and techniques for scaling and parallelizing transformation pipelines. Interviewers may assess problem solving, choice of algorithms and data structures, and pragmatic design for reliability and performance.
Distributed Data Processing and Optimization
Comprehensive knowledge of processing large datasets across a cluster and practical techniques for optimizing end to end data pipelines in frameworks such as Apache Spark. Candidates should understand distributed computation patterns such as MapReduce and embarrassingly parallel workloads, how work is partitioned across tasks and executors, and how partitioning strategies affect data locality and performance. They should explain how and when data shuffles occur, why shuffles are expensive, and how to minimize shuffle cost using narrow transformations, careful use of repartition and coalesce, broadcast joins for small lookup tables, and map side join approaches. Coverage should include join strategies and broadcast variables, avoiding wide transformations, caching versus persistence trade offs, handling data skew with salting and repartitioning, and selecting effective partition keys. Resource management and tuning topics include executor memory and overhead, cores per executor, degree of parallelism, number of partitions, task sizing, and trade offs between processing speed and resource usage. Fault tolerance and scaling topics include checkpointing, persistence for recovery, and strategies for horizontal scaling. Candidates should also demonstrate monitoring, debugging, and profiling skills using the framework user interface and logs to diagnose shuffles, stragglers, and skew, and to propose actionable tuning changes and coding patterns that scale in distributed environments.
Data Validation, Leakage Prevention & Statistical Rigor
Data validation and governance practices within data pipelines and analytics platforms, including schema validation, data quality checks, anomaly detection, lineage, and data quality metrics. Addresses leakage prevention in analytics and machine learning workflows (e.g., proper train/test separation, cross-validation strategies, and leakage risk mitigation) and emphasizes statistical rigor in analysis and modeling (experimental design, sampling, hypothesis testing, confidence intervals, and transparent reporting). Applicable to data engineering, analytics infrastructure, and ML-enabled products.
Data Quality Debugging and Root Cause Analysis
Focuses on investigative approaches and operational practices used when data or metrics are incorrect. Includes techniques for triage and root cause analysis such as comparing to historical baselines, segmenting data by dimensions, validating upstream sources and joins, replaying pipeline stages, checking pipeline timing and delays, and isolating schema change impacts. Candidates should discuss systematic debugging workflows, test and verification strategies, how to reproduce issues, how to build hypotheses and tests, and how to prioritize fixes and communication when incidents affect downstream consumers.
Data Pipelines and Feature Platforms
Designing and operating data pipelines and feature platforms involves engineering reliable, scalable systems that convert raw data into production ready features and deliver those features to both training and inference environments. Candidates should be able to discuss batch and streaming ingestion architectures, distributed processing approaches using systems such as Apache Spark and streaming engines, and orchestration patterns using workflow engines. Core topics include schema management and evolution, data validation and data quality monitoring, handling event time semantics and operational challenges such as late arriving data and data skew, stateful stream processing, windowing and watermarking, and strategies for idempotent and fault tolerant processing. The role of feature stores and feature platforms includes feature definition management, feature versioning, point in time correctness, consistency between training and serving, online low latency feature retrieval, offline materialization and backfilling, and trade offs between real time and offline computation. Feature engineering strategies, detection and mitigation of distribution shift, dataset versioning, metadata and discoverability, governance and compliance, and lineage and reproducibility are important areas. For senior and staff level candidates, design considerations expand to multi tenant platform architecture, platform application programming interfaces and onboarding, access control, resource management and cost optimization, scaling and partitioning strategies, caching and hot key mitigation, monitoring and observability including service level objectives, testing and continuous integration and continuous delivery for data pipelines, and operational practices for supporting hundreds of models across teams.
Data Pipeline Architecture
Design end to end data pipeline solutions from problem statement through implementation and operations, integrating ingestion transformation storage serving and consumption layers. Topics include source selection and connectors, ingestion patterns including batch streaming and micro batch, transformation steps such as cleaning enrichment aggregation and filtering, and loading targets such as analytic databases data warehouses data lakes or operational stores. Cover architecture patterns and trade offs including lambda kappa and micro batch, delivery semantics and fault tolerance, partitioning and scaling strategies, schema evolution and data modeling for analytic and operational consumers, and choices driven by freshness latency throughput cost and operational complexity. Operational concerns include orchestration and scheduling, reliability considerations such as error handling retries idempotence and backpressure, monitoring and alerting, deployment and runbook planning, and how components work together as a coherent maintainable system. Interview focus is on turning requirements into concrete architectures, technology selection, and trade off reasoning.