Senior Data Engineer / Analyst - USA

Cogniify•Scottsdale, AZ

92d•$121,700 - $162,200•Remote

About The Position

We’re looking for an experienced Senior Data Engineer/Analyst to lead the design and delivery of production-grade data platforms, pipelines, and analytics solutions that drive business intelligence and AI/ML capabilities across the organization. In this role, you will own critical data workstreams end to end, make key architectural decisions on data modeling, pipeline design, and platform selection, and collaborate with clients and stakeholders to translate business requirements into scalable data solutions. You will champion modern data stack practices using Snowflake, Databricks, dbt, and cloud-native services, while mentoring other engineers and driving the maturity of our data engineering, analytics, and DataOps capabilities.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
5–8+ years of professional experience in data engineering, analytics engineering, or a closely related role with significant production delivery.
Deep expertise in SQL and advanced data transformation techniques across analytical databases and data warehouses.
Extensive hands-on experience with Snowflake and/or Databricks in production environments, including architecture design, performance tuning, and cost optimization.
Strong experience with dbt (data build tool) for production-grade transformation, testing, documentation, and CI/CD workflows.
Proficiency in Python (Pandas, PySpark, Polars) and Apache Spark for large-scale data processing.
Experience designing and operating workflow orchestration using Airflow, Dagster, Prefect, or cloud-native equivalents.
Strong knowledge of data ingestion patterns and tools: Fivetran, Airbyte, CDC (Debezium), streaming (Kafka, Kinesis, Flink).
Experience with data visualization and BI platforms: Tableau, Looker, Power BI, or Sigma at enterprise scale.
Proven ability to work directly with clients and stakeholders and translate ambiguous business requirements into concrete data solutions.
Demonstrated ability to mentor engineers, lead design reviews, and influence data architecture direction.
Strong understanding of data governance, cataloging, data quality, and compliance frameworks.

Nice To Haves

Snowflake SnowPro Advanced or Architect, Databricks Data Engineer Professional, or AWS Data Analytics Specialty certification.
Experience with lakehouse architectures using Delta Lake, Apache Iceberg, or Apache Hudi.
Familiarity with real-time analytics and streaming architectures: Kafka, Spark Structured Streaming, Flink, Materialize, or ksqlDB.
Experience with metrics/semantic layers at scale: dbt Semantic Layer, Cube, MetricFlow, or LookML.
Experience designing data platforms that serve AI/ML workloads, including feature stores (Feast, Tecton), vector databases (Pinecone, Weaviate), and RAG data pipelines.
Familiarity with data mesh or data product architecture patterns in large organizations.
Experience with cloud data infrastructure across AWS (S3, Glue, Athena, Lake Formation, Redshift Serverless), Azure (ADLS, Synapse, Fabric, Purview), or GCP (BigQuery, Dataflow, Dataplex).
Knowledge of cost optimization and FinOps for data platforms (Snowflake credit management, Databricks cluster policies, spot instances).

Responsibilities

Lead the design, development, and optimization of enterprise-grade data pipelines and transformation layers using dbt, Apache Spark, Airflow, Dagster, and cloud-native orchestration services.
Architect and manage data platforms on Snowflake and/or Databricks, including warehouse design, lakehouse architecture, compute optimization, access governance, and cost management.
Define and enforce data modeling standards across the organization using Kimball dimensional modeling, Data Vault 2.0, Activity Schema, or hybrid approaches.
Design and implement end-to-end data ingestion strategies from diverse sources: transactional databases (CDC via Debezium, Fivetran), APIs, event streams (Kafka, Kinesis, Spark Structured Streaming), SaaS platforms, and unstructured data sources.
Build and maintain curated data products, metrics layers, and semantic models that enable self-service analytics across the organization.
Implement comprehensive data quality, observability, and lineage frameworks using dbt tests, Great Expectations, Monte Carlo, Datafold, Elementary, or Soda.
Collaborate with clients and business stakeholders to gather requirements, translate them into data architecture decisions, and drive technical strategy from ideation to production.
Lead the adoption of DataOps practices including CI/CD for data pipelines (GitHub Actions, dbt Cloud, Databricks Asset Bundles), automated testing, and environment promotion workflows.
Design and optimize analytics solutions and advanced dashboards using Tableau, Looker, Power BI, or Sigma Computing, ensuring performance and usability at scale.
Support AI/ML initiatives by designing and maintaining feature stores, training datasets, and model input/output data pipelines in collaboration with ML engineers.
Mentor junior and mid-level data engineers and analysts, conduct architecture and code reviews, and drive knowledge sharing across the team.
Drive data governance initiatives including cataloging (Alation, Atlan, DataHub, Unity Catalog), lineage tracking, PII management, and regulatory compliance (GDPR, CCPA, HIPAA).