23 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You’ll split time between coding, meetings, and checking dashboards. Mornings often start by looking at Airflow or JIRA to see which data jobs failed overnight and restarting or debugging them.
Afternoons go to building or testing models in Spark or Redshift, cleaning data in Alteryx or SQL, and then meeting product or analytics stakeholders to explain findings and plan the next dashboard or model run.
Expect to work with several: Apache Airflow for job scheduling, Apache Spark and Hive for large-scale data processing, and Amazon Redshift as a data warehouse. You’ll also use Kafka for streaming data and Alteryx for drag‑and‑drop data prep at some companies.
For team work you’ll see Atlassian JIRA for tickets and Confluence for docs. Learn SQL first, then Spark (PySpark), Airflow basics, and how Redshift queries are written.
The Bureau of Labor Statistics (BLS) reports 262,440 people employed in related roles with a median annual wage of $120,230. The lowest tenth earn about $67,240 and the top tenth about $199,130.
Pay varies by location, company, and experience—senior roles that own models and data platforms using Spark, Kafka, and Redshift sit near the top of that range.
Analytics engineers sit between data engineers and analysts. You build and maintain the data models and transformation code (like Spark jobs and Airflow DAGs) so analysts and data scientists can run queries and dashboards.
Data engineers focus more on pipelines and infrastructure (Kafka, Spark clusters), while data scientists focus on experimental models and research. You’ll combine coding, SQL modeling, and stakeholder communication.
Yes. You’ll apply machine learning techniques to predict churn or classify transactions, then monitor model performance in production. Use testing, versioning, and metrics (AUC, precision) to check models and Airflow to schedule retraining.
Be cautious with sensitive data: follow company privacy rules, avoid sharing raw PII in public prompts or unvetted AI services, and keep model explanations clear for stakeholders. Always log data provenance and model changes.
Begin with SQL and basic statistics, then learn a scripting language like Python. Practice building ETL jobs and data models on small datasets, then try Spark (PySpark) and Redshift for larger workloads.
Set up Airflow locally and write simple DAGs, explore Alteryx if you prefer visual tools, and use Git for version control. Build a portfolio: a clean dataset, a Redshift or Spark workflow, and a dashboard to show end‑to‑end work.
They test SQL problem solving (joins, aggregations), data modeling (star schema, consistency), and debugging ETL failures—expect questions about handling late-arriving data, duplicates, and schema changes.
You’ll also be asked to explain a past pipeline: which tools (Airflow, Spark, Redshift), how you tested and monitored it, and how you communicated results to stakeholders. Concrete examples beat theory.