1 to 3 years of experience in data engineering or related technical role.
Bachelor's degree in computer science, information systems, engineering, mathematics, or equivalent experience.
Hands-on experience with Databricks on professional or substantial academic projects.
Proficient in Python for data processing.
Experience with Apache Spark and PySpark for building transformations.
Strong SQL skills, including joins and aggregations.
Familiarity with cloud platforms like AWS, Azure, or Google Cloud.
Responsibilities
Build and maintain ETL pipelines in Databricks using Python and PySpark.
Ingest data from various sources into Delta Lake tables.
Implement medallion architecture patterns for data layers.
Write and optimize SQL for data transformations and reporting.
Add data quality checks and troubleshoot job failures.
Schedule and monitor workloads using Databricks Workflows.
Document pipelines and participate in client meetings to clarify requirements.
Benefits
Hybrid work model with client-facing opportunities.
Exposure to diverse data platforms and industries.
Opportunity to work with senior engineers and architects.
Professional development through code reviews and CI/CD contributions.
Engagement in client meetings for direct feedback and collaboration.
Full Job Description
**This is a hybrid role and requires work on-site with our clients.
About the role As a Consultant, Data Engineer, you will build and maintain data pipelines on Databricks for Infinitive clients. You will work on project teams with senior engineers, architects and business analysts, writing production Python, PySpark and SQL code that moves data from source systems into tables the client's analysts and data scientists use every day. The role is ideal for an engineer with one to three years of experience who wants client-facing work and regular exposure to new data platforms and industries. The client location is McLean, Virginia.What you will do
Build and maintain batch and incremental ETL pipelines in Databricks using Python, PySpark and Spark SQL.
Ingest data from relational databases, APIs, flat files and cloud storage (Amazon S3, Azure Data Lake Storage) into Delta Lake tables.
Implement bronze, silver and gold layers following medallion architecture patterns.
Write and tune SQL for transformations, data validation and reporting tables.
Add data quality checks, handle schema changes and troubleshoot failed jobs.
Schedule and monitor workloads with Databricks Workflows, working within Unity Catalog permissions.
Tune Spark jobs for performance and cost, including partitioning, file sizing and cluster configuration.
Use Git for version control, take part in code reviews and contribute to CI/CD pipelines.
Document pipelines, source-to-target mappings and runbooks so client teams can run what you build.
Join client meetings to clarify requirements, report status and walk through your work.
What you bring
1 to 3 years of professional experience in data engineering, software engineering or a related technical role.
Bachelor's degree in computer science, information systems, engineering, mathematics or a related field, or equivalent experience.
Hands-on experience with Databricks on a professional or substantial academic project.
Working proficiency in Python for data processing.
Experience building transformations with Apache Spark and PySpark (DataFrames and Spark SQL).
Strong SQL, including joins, window functions, CTEs and aggregations.
Understanding of ETL concepts such as incremental loads, slowly changing dimensions, dimensional modeling and data quality checks.
Familiarity with at least one cloud platform (AWS, Azure or Google Cloud).
Clear written and verbal communication, and comfort explaining technical work to non-technical stakeholders.
Authorization to work in the United States [confirm sponsorship policy].
Nice to have
Databricks Certified Data Engineer Associate certification.
Experience with Delta Lake features such as MERGE and time travel, or with Lakeflow Declarative Pipelines (formerly Delta Live Tables).
Exposure to Unity Catalog, dbt, Apache Airflow or Azure Data Factory.
Experience with Terraform or CI/CD tools such as GitHub Actions or Azure DevOps.
Prior consulting or client-facing experience.
Background in financial services, healthcare or media data.
Infinitive is required by law in some jurisdictions to include a reasonable estimate of the compensation range for this role. The determination of this range includes various factors not limited to skill set, level, experience, relevant training, and licensure and certifications. Compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range for this role in the U.S. is $75,000 - $120,000.00.
