Data Engineer Resume Keywords

What You Need to Know

Data engineers build pipelines that break in ways you never expect. A schema change in the source system breaks everything downstream. ETL jobs that ran fine for months suddenly fail because a data format changed. Airflow DAGs need careful dependency management—one failed task can block dozens of others. Data quality issues surface months later when analysts notice inconsistencies. Streaming data requires handling late-arriving events and out-of-order records. Data warehouses fill up faster than expected, requiring constant optimization. Schema evolution is tricky—adding a column shouldn't break existing queries, but it often does. Data engineering is the foundation of data science and analytics, but it's often less glamorous than building machine learning models. Data engineers build the infrastructure that enables data scientists and analysts to do their work. This requires understanding databases, distributed systems, and data processing frameworks. The work is technically challenging and critical for data-driven organizations. ETL (Extract, Transform, Load) processes are the core of data engineering, but they're more complex than they seem. Extracting data from source systems requires understanding their APIs, data formats, and access patterns. Some sources provide clean, well-documented APIs, while others require scraping or working with legacy systems. Rate limiting and error handling are essential because source systems can be unreliable. Transformations need to handle data quality issues like missing values, duplicates, and inconsistent formats. Loading data into warehouses requires understanding partitioning strategies and load patterns. Incremental loads are more efficient than full loads but require tracking what's already been loaded. Data quality is a constant concern in data engineering. Bad data leads to bad insights, so data quality checks are essential. But defining what "good" data means requires understanding the business domain. Data validation rules need to be tuned to catch real issues without creating false positives. Data profiling helps understand data characteristics, but it requires interpretation. Data lineage tracking helps understand where data comes from and how it's transformed, but it requires careful implementation. Monitoring data quality requires setting up alerts and dashboards, but determining thresholds requires judgment. Schema management is challenging because schemas evolve over time. Adding columns shouldn't break existing queries, but it often does. Backward compatibility is important but can limit flexibility. Schema registries help manage schema evolution, but they add complexity. Understanding when to break backward compatibility versus when to maintain it requires judgment. Data modeling requires understanding both the data and how it will be used. Normalized models reduce redundancy but can require complex joins. Denormalized models improve query performance but increase storage and update complexity. Star schemas and snowflake schemas are common for data warehouses, but choosing the right approach depends on use cases. Data partitioning strategies affect query performance and storage costs. Understanding access patterns is essential for effective data modeling. Data warehousing requires understanding different storage and query engines. Traditional data warehouses like Snowflake and BigQuery provide managed services but can be expensive at scale. Data lakes provide cheaper storage but require choosing query engines. Understanding the trade-offs between different solutions requires understanding use cases and cost structures. Optimizing warehouse performance requires understanding query patterns and data distribution. Partitioning and clustering strategies can dramatically improve performance but require careful design. Streaming data processing adds significant complexity compared to batch processing. Handling late-arriving events requires understanding event time versus processing time. Out-of-order events need to be handled correctly. Watermarks help determine when windows can be closed, but they require tuning. Exactly-once processing is difficult to achieve and often requires trade-offs with performance. Stream processing frameworks like Kafka Streams and Flink provide powerful capabilities but have steep learning curves. Orchestration tools like Airflow help manage complex workflows, but they require careful design. DAGs (Directed Acyclic Graphs) define task dependencies, but managing dependencies becomes complex as workflows grow. Task retries and failure handling need to be configured correctly. Monitoring workflow execution requires understanding which metrics matter. Debugging failed workflows requires understanding task logs and dependencies. Workflow versioning is important because workflows evolve over time. Data pipeline testing is challenging because data volumes are large and pipelines are complex. Unit testing individual transformations helps, but integration testing requires test data that represents production characteristics. Data quality tests verify that transformations produce expected results, but they need to be maintained as schemas evolve. Performance testing ensures pipelines can handle expected volumes, but predicting future volumes is difficult. Data governance is becoming increasingly important as regulations like GDPR require understanding what data is collected and how it's used. Data catalogs help document data assets, but they require maintenance. Access controls need to be configured to follow the principle of least privilege. Data retention policies need to be implemented and enforced. Understanding compliance requirements and implementing them correctly requires coordination with legal and compliance teams. Working as a data engineer requires broad technical knowledge and attention to detail. The work is often less visible than data science, but it's equally important. Success requires understanding databases, distributed systems, data processing frameworks, and data modeling. The field rewards those who can build reliable, scalable data infrastructure that enables data-driven decision making.