At Steampunk, a Data Engineer plays a pivotal role in designing, building, and optimizing the data architectures that power critical federal IT systems. Working at the intersection of human-centered design and robust DevSecOps, you will transform massive, complex government data streams into clean, actionable assets. Your work directly impacts public sector missions, enabling federal agencies to make data-driven decisions that affect millions of citizens daily.

As a Data Engineer, you will not just write ETL scripts; you will architect scalable cloud-native data platforms, design modern data warehouses, and establish rigorous data governance frameworks. Steampunk prides itself on bringing commercial-grade innovation to the federal space. This means you will work with cutting-edge tools in AWS, Azure, Snowflake, and modern pipeline orchestrators, solving complex data integration challenges that require both high performance and strict security compliance.

The ideal candidate enjoys the challenge of modernizing legacy government systems and translating complex business requirements into elegant data models. Whether you are joining as an ETL Engineer or a Senior Data Warehouse Engineer, you will collaborate closely with data scientists, cloud architects, and product owners to deliver secure, resilient, and highly performant data solutions.

The interview process at Steampunk evaluates your technical depth, architectural instincts, and consultative communication skills. While the exact questions will vary depending on your seniority and the specific federal project, the following representative questions highlight the key thematic patterns you should prepare for.

Data Modeling & Warehousing

This category evaluates your understanding of data storage paradigms, schema design, and how to structure data for optimal query performance.

• How do you decide between a star schema and a snowflake schema when designing a new data warehouse?
• Explain the concept of slowly changing dimensions (SCD) and how you would implement Type 2 SCD in a pipeline.

Preparing for an interview at Steampunk requires a balanced approach. You must demonstrate deep technical mastery while showing that you can operate effectively within a collaborative, agile consulting environment.

When preparing, focus on how you can demonstrate strength in the following core evaluation criteria:

Technical & Domain Expertise – You must show a deep, practical understanding of SQL, Python, data modeling, and cloud infrastructure. Interviewers want to see that you understand the underlying mechanics of the tools you use, not just how to write basic syntax.

Architectural Thinking – You need to demonstrate that you can design scalable, resilient systems. This means thinking about failure modes, security, data governance, and cost optimization when designing data pipelines and warehouses.

Consultative Communication – As a consultant, you must be able to articulate the "why" behind your technical decisions. You should be comfortable translating complex technical architectures into business value for federal clients.

Collaboration & Adaptability – Steampunk values a collaborative, human-centered culture. You should show that you can work effectively across multidisciplinary teams, embrace feedback, and adapt to shifting project requirements.

The interview process at Steampunk is designed to be thorough yet collaborative, focusing on both your technical capabilities and your cultural alignment with the firm's client-first philosophy. The progression moves from high-level alignment to deep-dive technical and behavioral evaluations.

Initially, you will speak with a recruiter to discuss your background, career goals, and alignment with Steampunk's core values. This conversation also covers logistical details such as salary expectations, location preferences in McLean, VA, and security clearance eligibility. Following this, you will proceed to a technical screening, which may involve a hands-on coding assessment or a deep-dive technical discussion focusing on SQL, Python, and data modeling.

The final stage consists of a series of interviews with senior engineers, architects, and project leaders. During this round, you will dive deep into system design, discuss complex architectural scenarios, and answer behavioral questions designed to evaluate your consulting and collaboration skills. Throughout the process, the hiring team aims to understand not just what you can build, but how you solve problems and work within a team.

The timeline above outlines the standard progression of the hiring process from the initial touchpoint to the final decision. Candidates should use this visual roadmap to pace their preparation, ensuring they focus on foundational coding skills early on and transition to high-level system design and behavioral stories for the final rounds. While the exact duration can vary based on federal contract timelines, the overall structure remains highly consistent.

To succeed in the Steampunk interview loop, you must perform well across several distinct technical and analytical domains.

Data Warehousing & Dimensional Modeling

At its core, data engineering at Steampunk involves structuring data so that it can be easily queried and analyzed. You must demonstrate a mastery of dimensional modeling concepts and data warehousing strategies.

Be ready to go over:

• Kimball Methodology – Star schemas, snowflake schemas, fact tables, and dimension tables.
• Slowly Changing Dimensions (SCD) – Implementing and managing Type 1, Type 2, and Type 3 dimensions.
• Partitioning & Indexing – How to structure physical storage in a database to optimize query performance and reduce compute costs.
• Advanced concepts (less common) – Data Vault 2.0 modeling, column-level security implementation, and managing semi-structured data variants in Snowflake.

Example scenarios:

• "Design a data warehouse schema for a federal agency tracking grant applications, payments, and compliance reviews over a ten-year period."
• "Explain how you would optimize a slow-running query that joins a multi-billion-row fact table with several high-cardinality dimensions."

ETL/ELT Pipeline Engineering

Building reliable, automated data pipelines is a daily responsibility for a Data Engineer. You must show that you can build pipelines that are secure, scalable, and easy to maintain.

Be ready to go over:

• Idempotency & Replayability – Designing pipelines that can be rerun safely without creating duplicate data or corrupting state.
• Data Quality & Validation – Implementing automated checks to catch schema drift, null values, or anomalous data before it reaches production.
• Orchestration – Using tools like Apache Airflow, Prefect, or AWS Step Functions to manage dependencies and schedule workflows.
• Advanced concepts (less common) – Stream processing with Apache Kafka, real-time CDC (Change Data Capture) implementation, and zero-downtime pipeline deployments.

Example scenarios:

• "Walk through the architectural design of a pipeline that ingests daily XML dumps from a legacy federal mainframe, validates the schema, and loads the clean data into an AWS S3 data lake."
• "How would you handle a scenario where an upstream data source suddenly changes its schema, causing your production pipeline to fail?"

Cloud Infrastructure & Security

Federal data environments require strict adherence to security standards (such as FedRAMP, HIPAA, or DoD Impact Levels). Your architectural designs must reflect these security-first principles.

Be ready to go over:

• Cloud Data Services – Utilizing key data services within AWS (S3, Redshift, Athena, Glue) or Azure (Blob Storage, Synapse, Data Factory).
• Data Encryption – Implementing encryption at rest and in transit using customer-managed keys.
• Identity & Access Management (IAM) – Designing least-privilege access policies for data pipelines and database users.
• Advanced concepts (less common) – Multi-region data replication, cloud cost optimization strategies, and infrastructure-as-code (IaC) for data platforms using Terraform.

Example scenarios:

• "How would you design a secure data sharing mechanism between two different federal agencies operating in separate AWS accounts?"
• "Describe how you would architect a serverless data processing pipeline using AWS Lambda and Athena to minimize operational overhead and cost."