Data Engineer II

Must Haves:

·       Strong dbt development using SQL and Jinja.

·       Strong SQL and Python fundamentals.

·       Snowflake experience and an understanding of how modeled data supports reusable data products.

·       Data modeling and semantic-model experience, not merely extraction and loading.

·       Critical thinking, adaptability, project ownership, and comfort working directly with stakeholders.

Helpful but Flexible:

·       AWS Glue and AWS data-lake experience. Azure is acceptable if the candidate understands data-flow and lake concepts.

·       Finance, accounting, cash, P&L, or land-data experience shortens the learning curve.

·       Iceberg and experience exposing or loading transformed data into Snowflake.

·       Power BI awareness, although client is intentionally moving away from producing many custom reports.

·       Interest in agentic data products and MCP servers.

Reject or Probe Carefully:

·       Traditional ETL engineers who are strong in ingestion but light on dbt, dimensional/semantic modeling, or business context.

·       BI-only candidates whose main strength is Power BI report development.

·       Candidates who depend on scripted or AI-fed interview answers and cannot reason through a new scenario.

Your Responsibilities on the Team:

·       Design, implement and support an analytical data infrastructure and working knowledge of Modern Data Warehouse concepts.

·       Design, build, and maintain efficient and scalable data pipelines and ETL processes to process large volumes of structured and unstructured data.

·       Optimize data storage and retrieval methods to ensure performance, scalability, and cost-efficiency.

·       Manage AWS resources including EC2, S3, Glue, Lambda, API’s, IAM, Cloud Watch etc.

·       Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies.

·       Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency

·       Collaborate with Data Scientists and Business Intelligence Engineers (BIEs) to recognize and help adopt best practices in reporting and analysis.

·       Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers.

·       Maintain internal reporting platforms/tools including troubleshooting and development. Interact with internal users to establish and clarify requirements in order to develop report specifications.

·       Work with Engineering partners to help shape and implement the development of BI infrastructure including Data Warehousing, reporting and analytics platforms.

·       Contribute to the development of the BI tools, skills, culture and impact.

·       Write advanced SQL queries and Python code to develop solutions.

·       Working Knowledge of Snowflake.

·       Collaborate across teams to align AI initiatives with organizational goals and understanding of AI concepts

·       Knowledge on continuous integration/continuous delivery (CI/CD) pipelines and working on deployments when necessary.

Requirements:

·       Bachelor's degree in Computer Science, Information Technology, or a related field.

·       3-5 years of experience in data engineering or a related role, with demonstrated success in delivering data solutions.

·       AWS Glue, Lambda, S3, EC2, CloudWatch, Cloud Trail.

·       Dbt, Snowflake, SQL, Python, Qlik.

·       Proficient in SQL, with the ability to write complex queries, perform query optimization, and conduct performance tuning.

·       Experience with NoSQL databases, such as MongoDB, Cassandra, or DynamoDB, and an understanding of their appropriate use cases.

·       Strong programming skills in Python, Java, or Scala, with experience in data processing frameworks (e.g., Apache Spark, Hadoop).

·       Experience with cloud platforms (AWS, Azure, Google Cloud Platform) and data services, such as AWS Redshift, Azure Synapse, or Google BigQuery.

·       Knowledge of big data technologies, including Hadoop, Spark, Kafka, and HBase, with experience in distributed data processing.

·       Familiarity with data orchestration tools, such as Apache Airflow for scheduling and managing data workflows.

·       Experience with data versioning and testing tools, such as DVC (Data Version Control) and dbt (data build tool).

·       Understanding of data security practices, including encryption, access controls, and data masking.