Mid-Level Data Engineer / Analyst - USA

Cogniify•Scottsdale, AZ

92d•$91,900 - $144,100•Remote

About The Position

We’re seeking a Mid-Level Data Engineer/Analyst to independently design, build, and optimize data pipelines and analytics solutions that power business intelligence and AI/ML initiatives. In this role, you will own key data workstreams end to end, build production-grade transformation layers using dbt and Spark, manage data infrastructure on Snowflake and Databricks, and collaborate with analysts, data scientists, and product teams to deliver reliable, well-governed, and high-quality data products. You will also contribute to the maturity of our DataOps and data observability practices.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
3–5 years of professional experience in data engineering, analytics engineering, or a closely related role with production delivery.
Strong proficiency in SQL and experience writing complex transformations, window functions, CTEs, and performance-tuned queries.
Hands-on experience with at least one modern data platform: Snowflake (strongly preferred), Databricks, BigQuery, or Redshift.
Experience with dbt (data build tool) for data transformation, testing, and documentation in production environments.
Working knowledge of Python (Pandas, PySpark, or Polars) for data processing and pipeline development.
Experience with workflow orchestration tools: Airflow, Dagster, Prefect, or cloud-native equivalents (AWS Step Functions, Azure Data Factory).
Familiarity with data ingestion tools and patterns: Fivetran, Airbyte, CDC (Debezium), or streaming ingestion (Kafka, Kinesis).
Experience with data visualization and BI tools: Tableau, Looker, Power BI, or Sigma.
Understanding of data modeling methodologies (Kimball, Data Vault, OBT) and data warehousing best practices.
Familiarity with version control (Git), CI/CD for data, and Agile development workflows.

Nice To Haves

Snowflake SnowPro Core, Databricks Data Engineer Associate, or AWS Data Analytics Specialty certification.
Experience with Apache Spark and Databricks for large-scale data processing and lakehouse architectures.
Familiarity with data cataloging and governance tools: Alation, Atlan, DataHub, Collibra, or Databricks Unity Catalog.
Experience with data observability platforms: Monte Carlo, Datafold, Soda, or Elementary.
Exposure to streaming data pipelines using Kafka, Spark Structured Streaming, Flink, or Kinesis.
Experience with metrics/semantic layers: dbt Semantic Layer, Cube, or Looker Modeling Language (LookML).
Knowledge of cloud data infrastructure: AWS (S3, Glue, Athena, Redshift, Lake Formation), Azure (ADLS, Synapse, Data Factory), or GCP (GCS, Dataflow, BigQuery).

Responsibilities

Design, build, and maintain production-grade ETL/ELT pipelines using dbt, Apache Spark (PySpark), Airflow, Dagster, or Prefect.
Develop and optimize data models on Snowflake, Databricks, BigQuery, or Redshift following dimensional modeling, data vault, or One Big Table patterns.
Implement and manage data ingestion from diverse sources including databases, REST/GraphQL APIs, event streams (Kafka, Kinesis), SaaS platforms, and flat files using Fivetran, Airbyte, or custom connectors.
Build and maintain semantic/metrics layers and curated data products for analytics, reporting, and self-service consumption.
Implement data quality, testing, and observability frameworks using dbt tests, Great Expectations, Soda, Monte Carlo, or Datafold.
Create advanced dashboards, reports, and analytical visualizations using Tableau, Looker, Power BI, or Sigma Computing.
Optimize query performance, pipeline efficiency, and cloud data platform costs across Snowflake, Databricks, or BigQuery.
Collaborate with data scientists and ML engineers to prepare and serve feature datasets for machine learning models.
Implement DataOps practices including CI/CD for data pipelines, version-controlled transformations, and automated testing.
Write production-quality Python and SQL code with proper testing, documentation, and error handling.
Support data governance initiatives including cataloging, lineage tracking, access controls, and PII management using tools like Alation, Atlan, DataHub, or Unity Catalog.