Argentina 
Brazil 
Mexico 
Colombia 
A data engineer builds and maintains the pipelines that move data from source systems into a warehouse or lake, where analysts, applications, and AI models can use it. A good job description names the stack, the scale of the data, the teams the engineer will support, and what success looks like in the first six months.
Below is a template you can copy. After it, you will find what US companies are asking for in 2026, how the role changes by seniority, interview questions written for the interviewer, and a scorecard to compare candidates.
Data engineer job description template
Replace the text in brackets and delete any line that is not true for your team. A short, accurate description attracts stronger candidates than a long wish list.
About the role
We are looking for a [Senior] Data Engineer to design, build, and run the data pipelines behind [our product, analytics, and machine learning work]. You will own ingestion from [source systems], transformations in [dbt or Spark], and the reliability of the data our [analytics, product, and data science] teams use every day. You will report to [role] and work closely with [teams].
Responsibilities
- Build and maintain batch and streaming pipelines from [sources] into [Snowflake, BigQuery, or Databricks].
- Model data for analytics and product use cases, and keep models documented and tested.
- Orchestrate workflows in [Airflow, Dagster, or Prefect] and monitor them in production.
- Set up data quality checks, freshness alerts, and a clear process for handling failures.
- Improve query performance and keep warehouse costs under control.
- Work with analysts, data scientists, and engineers to turn business questions into reliable datasets.
- Document pipelines and data models so teammates, and the AI tools they use, can rely on them.
Must-have qualifications
- [4 to 5]+ years of experience in data engineering or a closely related role.
- Strong SQL and Python.
- Production experience with a cloud data warehouse such as [Snowflake, BigQuery, or Redshift].
- Experience with an orchestration tool such as [Airflow, Dagster, or Prefect].
- Solid data modeling fundamentals, such as dimensional modeling.
- Hands-on experience with [AWS, Google Cloud, or Azure].
- Clear written communication in English.
Nice to have
- dbt for modular, tested transformations.
- Spark or Databricks for large-scale processing.
- Streaming with Kafka, Kinesis, or Flink.
- Infrastructure as code with Terraform.
- Experience with data in [your industry].
- Daily use of AI coding assistants as part of the development workflow.
What we offer
[Compensation range, if you can share it], [location or time zone requirements], [benefits], and [how the team works day to day].
What US companies ask for in data engineers in 2026
We reviewed the requirements in the data engineering roles US companies asked BEON.tech to fill between 2023 and 2026. A few patterns stand out:
- Python and SQL are the baseline. Each appears in about four out of five roles, so they belong in the must-haves, not in the list of differentiators.
- Snowflake is the most requested warehouse. It shows up in more than half of the roles, followed by BigQuery and Redshift.
- AWS is the most common cloud. It appears in more than half of the roles. Azure and Google Cloud come up less often.
- Airflow is still the default orchestrator, but several recent roles name Dagster or Prefect instead.
- dbt and Databricks are a recent shift. They appear mostly in roles opened in 2025 and 2026.
- Data modeling is an explicit requirement in close to half of the roles, often with dimensional modeling or Data Vault named.
- AI tools entered the requirements in 2026. Some roles now list daily use of AI coding assistants, such as Claude Code or Cursor, and one asked for documentation clear enough for AI agents to use as ground truth.
- Five years is the usual bar. Most roles asked for about five years of experience, with a range from three to ten.
- Industry experience is usually a bonus. Ad tech, insurance, energy, and IoT experience showed up mostly as nice-to-haves, not requirements.
In practice, you can keep the must-haves short: SQL, Python, one warehouse, one orchestrator, and data modeling. List other tools you use as nice-to-haves unless the engineer will own them from day one.
How the role changes by seniority
| Level | Scope | What to look for in the interview |
|---|---|---|
| Mid-level (3 to 5 years) | Builds and maintains pipelines that others designed. Owns well-scoped tasks. | Clean SQL and Python, and a clear explanation of why a pipeline failed. |
| Senior (5 to 8 years) | Designs pipelines and models end to end. Sets testing and monitoring standards and mentors others. | Trade-offs between batch and streaming, cost awareness, and sound modeling decisions. |
| Lead or staff (8+ years) | Owns the data platform: tool choices, migrations, and data contracts across teams. | Platform decisions they made, what they would change, and how they aligned other teams. |
Data engineer interview questions for the interviewer
These questions are written for the person running the interview. Each one lists what a strong answer covers and a warning sign. There are no model answers to memorize. The goal is to hear how the candidate reasons about real problems.
Pipelines and reliability
- “Walk me through a pipeline you built that broke in production. How did you find out, and what did you change afterward?” A strong answer covers how the failure was detected, the root cause, a fix at the source, and what they added to prevent it, such as tests or alerts. Warning sign: the story ends with blaming another team.
- “How do you make a pipeline safe to re-run?” Listen for idempotency, merge or partition-overwrite strategies, deduplication keys, and how they handle backfills. Warning sign: “we just run it again.”
- “A daily job that took 20 minutes now takes three hours. Where do you start?” A strong answer checks what changed first: data volume, query plans, skew, incremental logic. Warning sign: going straight to a bigger cluster.
Modeling and SQL
- “How would you model orders and customers for analysts who need daily revenue by segment?” Look for a clear grain, facts and dimensions, and how they handle customers who change segment over time. Warning sign: one wide table with no discussion of grain.
- “Write a query that returns the latest record for each customer.” A strong answer uses window functions and handles ties and nulls. Warning sign: a working query they cannot explain.
- “When would you transform data in the warehouse with dbt instead of in Python before loading?” Listen for trade-offs around testing, cost, and who maintains the logic.
Architecture and cost
- “When is streaming worth the extra complexity?” A strong answer ties the choice to a real latency requirement and counts the operational cost. Warning sign: streaming by default.
- “How do you keep warehouse costs under control?” Listen for partitioning or clustering, incremental models, usage monitoring, and right-sized compute. Warning sign: they have never looked at the bill.
Working with AI tools
- “Show me how you use an AI coding assistant on a data task. How do you check its output?” A strong answer validates generated SQL and code with tests and data checks, and knows where these tools fail, such as subtle join logic or edge cases. Warning sign: accepting output without verification. For a fuller framework, see how to evaluate engineers who work with AI agents.
Collaboration
- “Tell me about a time an analyst’s number did not match yours.” Look for tracing the definitions, agreeing on one metric, and documenting it. Warning sign: dismissing the business user.
A practical exercise
A short, realistic task tells you more than a whiteboard puzzle. Give candidates a small dataset with a few quality problems, such as duplicates, late-arriving records, and a changed column, and ask them to build an incremental model with tests. Allow AI tools, then ask what they accepted, what they changed, and why. Keep a take-home under three hours, or run it live in 60 to 90 minutes.
Data engineer interview scorecard
Score each criterion from 1 (no evidence) to 4 (strong evidence). Agree on the weights before the first interview, not after.
| Criterion | What a 4 looks like | Suggested weight |
|---|---|---|
| SQL and data modeling | Chooses the right grain, writes clear and efficient queries, and explains the trade-offs. | 25% |
| Pipeline design and reliability | Designs idempotent, observable pipelines and has handled real incidents. | 25% |
| Python and engineering practices | Writes readable, tested code and works with Git, code review, and CI. | 15% |
| Cloud and tooling | Has hands-on experience with your warehouse, orchestrator, and cloud. | 15% |
| AI-assisted workflow | Uses AI tools to move faster and verifies what they produce. | 10% |
| Communication | Explains technical choices to non-engineers and documents as they go. | 10% |
Hiring a data engineer through BEON.tech
If you would rather skip sourcing and screening, BEON.tech places senior data engineers from Latin America who work in US time zones. For the full process, read our guide on how to hire a data engineer, compare the role with a data scientist in data engineer vs. data scientist, or see how we help companies hire senior data engineers.
FAQ
What does a data engineer do?
A data engineer designs, builds, and runs the systems that collect, clean, and move data so other teams can use it. That includes ingestion from source systems, transformations, data modeling, orchestration, and monitoring. The goal is data that is accurate, fresh, and easy to query for analytics, products, and machine learning.
What should a data engineer job description include?
Include the purpose of the role, the main responsibilities, the stack the engineer will work with, must-have and nice-to-have qualifications, who the role reports to, and what you offer. Name your real tools, such as your warehouse and orchestrator, and keep the must-haves to what the engineer will use from the first month.
How many years of experience should a senior data engineer have?
Most senior data engineering roles ask for about five years of experience. In the roles US companies asked BEON.tech to fill between 2023 and 2026, requirements ranged from three to ten years, with five as the most common bar. Scope matters more than years: look for someone who has designed and owned pipelines end to end.
What is the difference between a data engineer and a data scientist?
A data engineer builds the infrastructure and pipelines that make data available and reliable. A data scientist uses that data to answer questions and build models. Many teams need both, and hiring a data scientist before the pipelines are in place usually slows the data scientist down. For a detailed comparison, see data engineer vs. data scientist.
