Top Senior Data Engineer Jobs in Memphis, TN

Own and scale Provable's data platform on GCP: build and optimize ingestion pipelines from blockchain indexers, design privacy-aware data models, manage Postgres and BigQuery, develop high-performance data-serving APIs, ensure data quality and observability, and use IaC (Terraform, Helm) and Kubernetes to deploy and cost-optimize environments.

The Senior Data Engineer will design and implement data processing pipelines using Java and Spark, optimize performance, and mentor junior engineers while ensuring compliance with data governance standards.

Founding data engineer to design and build Payabli's data platform: architect lakehouse/warehouse, build batch and streaming pipelines, model canonical datasets, ensure data quality/observability, enforce access/masking/lineage for regulated financial data, and enable analytics/ML feature pipelines while establishing team standards and CI/CD.

As a Senior Data Engineer, you'll design and maintain scalable data pipelines, optimize workflows with dbt and Snowflake, and mentor teams across functions to enhance data reliability and performance.

The role involves building and optimizing data pipelines, modeling datasets, applying machine learning techniques, and supporting data strategies. Candidates should have experience with Python, data lakes, warehousing, and related technologies.

Design, build, and maintain scalable data pipelines, data models, ETL/ELT, and BI solutions. Collaborate with data scientists and stakeholders to improve data quality, accessibility, and PHI security. Develop and document workflows, optimize processing/storage, support data warehouse and visualization, and adopt AI tools to drive data-informed outcomes.

Lead data engineering efforts: design, build, optimize, and support enterprise data pipelines and data warehouse solutions. Provide technical leadership, set data management and architecture standards, collaborate with BI and business analysts, troubleshoot ETL processes, and drive selection/implementation of analytical systems across cross-functional projects.

Build and maintain production data pipelines and foundations for People Analytics and AI-driven tools. Integrate HR systems (Workday, Greenhouse), design data models, ensure governance for sensitive employee data, support data science and reporting, and deliver dashboards and Streamlit apps. Optimize queries, enable LLM consumption with quality and latency controls, and collaborate cross-functionally to transition AI prototypes to production.

Design, build, and maintain dbt data models and scalable ELT pipelines; optimize performance-aware SQL on Snowflake; ensure observability, testing, and CI/CD; partner with product and engineering teams to meet analytic needs, mentor engineers, document business logic, and troubleshoot ingestion, transformation, and warehouse issues.

Design, build, and operate scalable data platform services and ETL/ELT pipelines that transform billions of events into trusted, low-latency data products. Own data quality, observability, SLAs, incident response, and platform tooling, drive architectural decisions, and mentor engineers to enable self-serve analytics and product capabilities across the organization.

Design, build, and optimize scalable data ingestion and transformation pipelines in a medallion (Bronze/Silver/Gold) architecture on Microsoft Fabric/OneLake. Ensure HIPAA-compliant, multi-tenant data isolation, data quality monitoring, lineage and governance. Partner with Data Science, ML Engineering, and Software Engineering to deliver feature stores, ML-ready datasets, and production-grade data delivery for analytics and scoring.

The Senior Data Engineer will construct and optimize data pipelines, work with cross-functional teams, ensure data quality and governance, and mentor junior engineers while leading advanced analytics initiatives.

Design, build, operate, and optimize large-scale data pipelines and foundational data products for Komodo's Healthcare Map. Improve pipeline reliability, observability, and performance; transform claims and EHR datasets; implement data quality, lineage, and monitoring; debug production workflows; collaborate with product and engineering teams; and enable downstream analytics and AI/ML use cases.

Design and maintain data pipelines (SQL, dbt, BigQuery), ensure data quality and event tracking, analyze A/B tests, build funnel and attribution reporting, create Looker dashboards, and support LLM-enriched data models for smarter targeting and segmentation.

Technical lead on the Distribution Center data team building and scaling ETLs and data integrations using PySpark and DBT. Design robust pipelines to process large customer datasets, improve onboarding tooling, mentor engineers, manage contractors, and collaborate cross-functionally to support ML-driven grocery solutions.

Design and build cloud-based data applications and pipelines to analyze clinical data, enrich and provision datasets, and support clinical/operational processes. Mentor junior engineers, drive R&D for repeatable templates, collaborate with Product/Platform/Architecture, and prioritize source control, documentation, and simple solutions using modern data, ETL, and LLM tooling.

Design, build, and operate large-scale data pipelines and ETL processes to normalize varied partner datasets. Lead technical decisions, mentor engineers, implement testing and monitoring, manage data warehouses/lakes, and collaborate cross-functionally to deliver robust data infrastructure that supports ML and product teams.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Lead Data Engineer builds and maintains scalable data pipelines and lakehouse/warehouse platforms (Databricks) to support analytics, BI, reporting, and AI. Responsibilities include data modeling, ETL/ELT, medallion architecture, orchestration and monitoring (Airflow/Cron), performance tuning, CDC/incremental loads, data quality, and leading/mentoring a team while collaborating with stakeholders and maintaining documentation.

Design, build, and maintain ETL/ELT pipelines and data integration workflows to Snowflake using Azure Data Factory and DBT. Write optimized SQL, monitor and optimize pipeline performance, enforce data quality and governance, and provide technical leadership to junior engineers while collaborating with analysts, architects, and DevOps in cloud environments.

Design, build, and optimize scalable cloud data pipelines and infrastructure. Maintain and refactor SQL and ETL processes, automate data workflows, ensure data quality/security, and troubleshoot complex issues. Provide technical leadership and mentor junior engineers while collaborating with stakeholders to translate requirements.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from SQL/NoSQL sources, data lakes, and streaming platforms. Ensure data quality, error handling, performance tuning, and collaborate with analysts, scientists, and architects to deliver scalable data solutions.

Design, implement, and maintain PySpark applications to automate large-scale financial data reconciliations. Build transformation and matching algorithms, integrate with rules engines, analyze data gaps, and collaborate with analysts and architects to ensure data quality and system resilience.

Design, implement, and maintain PySpark-based data reconciliation solutions for financial systems. Build matching algorithms, integrate with rules engines, process large distributed datasets, identify and resolve discrepancies, and collaborate with analysts and architects to improve data quality and governance.

Design, build, and maintain scalable ETL/ELT pipelines into Snowflake, enforce data quality, optimize data models and query performance for Power BI reporting, produce documentation, and troubleshoot data issues while collaborating with stakeholders.