Data Engineer - Cloud Data Platforms & AI

Slalom; Baltimore (MD); Hybrid; USD 100,000 - 115,000; Full time

Benefits offered by this job

Well-being reimbursement

Health, dental, & vision coverage

401(k) with match

Paid holidays and time off

Job summary

Slalom is seeking a Data Engineer to design, build, and operate scalable data solutions across AWS, Azure, GCP, Databricks, and Snowflake. You will translate business requirements into maintainable engineering tasks and work with multidisciplinary teams to deliver cloud-native data platforms that support analytics, AI, and ML initiatives.

You will apply modern data engineering practices, contribute to reusable patterns, and participate in client engagements to drive data-driven outcomes.

Qualifications

• Experience with at least one major cloud or data platform such as AWS, Azure, GCP, Databricks or Snowflake.
• Proficiency in SQL, Python, Java and/or Scala.
• Experience with data modeling, ETL/ELT, distributed processing, lakehouse architectures, streaming, APIs, orchestration, or data platform modernization.
• Familiarity with Spark, Kafka, Airflow, dbt, Dagster, Azure Data Factory, BigQuery, Redshift, Synapse, Microsoft Fabric, Delta Lake, Unity Catalog, Dataplex, Dataflow, Pub/Sub, Kinesis, Kubernetes, Terraform, or comparable tools.
• Understanding of how high-quality, secure, governed data foundations support AI, ML, Generative AI, retrieval-augmented generation, and agentic solutions.
• Experience with AI-enabled or agentic engineering practices, including using AI-assisted development tools and designing data workflows or platforms that can support intelligent agents and automated decision workflows.
• A commitment to responsible engineering, including privacy, security, transparency, testing, monitoring, and appropriate human oversight for AI-enabled solutions.
• Strong problem-solving, communication, and collaboration skills, with an ability to explain technical concepts to different audiences.
• A curious, growth-oriented mindset and willingness to learn new platforms, tools, and delivery approaches.

Responsibilities

• Collaborate with clients, architects, and multidisciplinary delivery teams to design, build, and operate scalable data solutions.
• Design and implement cloud-native data platforms, data lakes, lakehouses, warehouses, and data products.
• Build reliable batch and streaming data pipelines, integrations, and orchestration workflows.
• Translate business and technical requirements into maintainable engineering solutions and tasks.
• Develop solutions across AWS, Azure, GCP, Databricks, and Snowflake based on client needs.
• Apply software engineering practices: version control, testing, CI/CD, infrastructure as code, observability, documentation, and secure development.
• Use AI-enabled engineering approaches to improve delivery speed, quality, testing, and maintainability with human review.
• Design, build, and operate governed data platforms that support analytics, AI, ML, Generative AI, and agentic workloads.
• Enable data quality, metadata, lineage, security, privacy governance, performance, reliability, and cost-effective operations.
• Participate in technical discovery, design sessions, code reviews, demonstrations, and knowledge sharing.
• Contribute to reusable patterns, accelerators, and engineering communities within Slalom.

Skills

Cloud platforms

SQL

Python/Java/Scala

Data engineering

AI-enabled engineering

Communication

Growth mindset

Tools

Spark

Kafka

Airflow

dbt

Dagster

Azure Data Factory

BigQuery

Redshift

Synapse

Dataplex

Dataflow

Pub/Sub

Kinesis

Kubernetes

Terraform

Job description

Slalom is seeking a Data Engineer to design, build, and operate scalable data solutions across AWS, Azure, GCP, Databricks, and Snowflake. You will translate business requirements into maintainable engineering tasks and work with multidisciplinary teams to deliver cloud-native data platforms that support analytics, AI, and ML initiatives.

You will apply modern data engineering practices, contribute to reusable patterns, and participate in client engagements to drive data-driven outcomes.