Data engineer - USA

Cogniify•,

1d•$133,500 - $140,000•Remote

About The Position

Cogniify is hiring a Mid Level Data Engineer to build reliable data pipelines and analytics datasets for reporting, business decisions and AI initiatives. You will own data work from ingestion through transformation and delivery, working with analysts, data scientists and engineers to make large datasets accurate, accessible and ready for use. This is a hands-on role for someone who enjoys both data engineering and the analytical questions behind the data.

Requirements

3 to 6 years of professional experience in data engineering, analytics engineering or a related data role with production delivery.
Strong SQL skills and experience writing complex transformations and improving query performance.
Hands-on experience with a cloud data platform. Snowflake is preferred; Databricks, BigQuery or Redshift experience is also relevant.
Production experience with dbt for transformation, testing and documentation.
Working knowledge of Python and either Pandas or PySpark for data processing.
Experience scheduling pipelines with Airflow, Dagster, Prefect or a similar orchestration tool.
A good understanding of data modeling and how to build datasets that analysts and business teams can use.

Nice To Haves

Apache Spark, Databricks and large-scale data processing.
Data quality or observability tools such as Great Expectations, Soda or Monte Carlo.
Streaming data with Kafka or Kinesis, or ingestion tools such as Fivetran or Airbyte.
Experience preparing data for ML features, AI search, embeddings or RAG applications.
Cloud services across AWS, Azure or Google Cloud, and data governance tools such as Unity Catalog or DataHub.

Responsibilities

Build and maintain ETL/ELT pipelines using SQL, Python, dbt and tools such as Apache Spark, PySpark or Airflow.
Ingest data from databases, APIs, SaaS tools and event streams using connectors or custom pipelines.
Develop tested data models and curated datasets in Snowflake, Databricks, BigQuery or Redshift for reporting and self-service analytics.
Work with data scientists and ML engineers to prepare feature datasets for model training and inference.
Prepare and refresh structured business data that can support AI search, retrieval-augmented generation (RAG) or other Generative AI applications.
Build clear dashboards and analyses in Tableau, Looker, Power BI or similar tools when the work calls for it.
Add data quality checks, monitoring and documentation so teams can trust the data and identify pipeline issues early.
Improve query speed and pipeline cost; use Git, code reviews and CI/CD to release changes safely.
Help manage data access, lineage and sensitive information, including personally identifiable information (PII).