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Lead Data Engineer

  • Lead Data Engineer job type Posted on: Oct 12, 2026
  • Lead Data Engineer experience level Openkyber
  • Lead Data Engineer employment type Atlanta, Georgia
  • Lead Data Engineer employment type Remote
  • Lead Data Engineer salary Full-time

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Job Title :

Lead Data Engineer

Job Type :

Full-Time

Job Location :

Atlanta Georgia United States

Remote :

Yes

Jobcon Logo Job Description :

Job Title: Senior Databricks Engineer
Location: 100% Remote
Interview Process: Video (2 Rounds)

Job Description: Lead/Senior Databricks Data Engineer Position: Lead Databricks Data Engineer / Databricks Architect Experience: 10+ Years Location: Remote

Job Summary We are looking for an experienced Lead Databricks Data Engineer / Databricks Architect with 10+ years of overall experience in Data Engineering and strong hands-on expertise in Databricks, Apache Spark, PySpark, SQL, Delta Lake, and cloud-based data platforms. The candidate will be responsible for designing and implementing scalable Lakehouse architectures, enterprise data pipelines, data integration solutions, governance frameworks, and high-performance analytics platforms using Databricks.

Key Responsibilities
  • Design and develop scalable data engineering solutions using Databricks and Lakehouse architecture.
  • Build robust ETL/ELT pipelines using PySpark, Spark SQL, Python, and SQL.
  • Design and implement Bronze, Silver, and Gold/Medallion architecture.
  • Develop and optimize Delta Lake tables, including MERGE, schema evolution, Change Data Feed, and incremental processing.
  • Build batch and real-time/streaming pipelines using Structured Streaming, Auto Loader, and Lakeflow.
  • Develop and manage Databricks Jobs/Workflows for pipeline orchestration, scheduling, dependencies, retries, and monitoring.
  • Implement enterprise data governance using Unity Catalog, including access control, data lineage, auditing, catalogs, schemas, and external locations.
  • Perform Spark and Databricks performance tuning, including cluster configuration, partitioning, caching, query optimization, Photon, and workload optimization.
  • Design data models supporting Data Warehousing, BI, Analytics, and AI/ML workloads.
  • Integrate Databricks with cloud platforms such as AWS, Azure, or Google Cloud Platform.
  • Work with cloud services such as AWS S3, Azure ADLS Gen2, Azure Data Factory, AWS Glue, Synapse, Event Hubs/Kafka/Kinesis, as applicable.
  • Implement CI/CD pipelines using Git, Azure DevOps/GitHub/Jenkins and Databricks deployment capabilities.
  • Work with Terraform/IaC for infrastructure provisioning and automation.
  • Troubleshoot production pipeline failures, performance issues, data-quality problems, and Spark/cluster issues.
  • Establish data quality, monitoring, logging, and observability practices.
  • Provide technical leadership, code reviews, architecture guidance, and mentorship to junior/mid-level engineers.
  • Collaborate with Data Architects, Data Scientists, Business Analysts, DevOps teams, and application teams.
Required Technical Skills
  • Databricks
  • Databricks Lakehouse Platform
  • Delta Lake
  • Unity Catalog
  • Databricks Workflows/Jobs
  • Lakeflow / Delta Live Tables
  • Auto Loader
  • Databricks SQL
  • Databricks notebooks
  • Databricks Asset Bundles
  • Photon
  • Cluster/workload optimization
  • Big Data
  • Apache Spark
  • PySpark
  • Spark SQL
  • Structured Streaming
  • Kafka
  • Batch and real-time data processing
  • Python
  • SQL
  • PySpark
  • Scala
  • AWS: S3, Glue, EMR, Lambda, Redshift, IAM, Kinesis
  • Azure: ADLS Gen2, ADF, Synapse, Azure DevOps, Event Hubs, Key Vault
  • Google Cloud Platform: GCS, BigQuery, Dataflow, Pub/Sub
  • ETL/ELT
  • Data Warehousing
  • Dimensional Modeling
  • Data Lake/Lakehouse
  • Medallion Architecture
  • CDC
  • Data Quality
  • Data Governance
  • Metadata and Data Lineage
  • Git
  • Azure DevOps / GitHub
  • Jenkins
  • Terraform
  • CI/CD automation
  • Infrastructure as Code
Preferred / Nice-to-Have Skills
  • MLflow
  • Databricks Machine Learning
  • Feature Store
  • Mosaic AI / GenAI
  • dbt
  • Apache Airflow
  • Power BI / Tableau
  • Delta Sharing
  • Lakehouse Federation
  • Liquid Clustering
  • Data security, PII masking

Qualifications

Bachelor's degree in Computer Science, Engineering, Information Technology, or related field. 10+ years of experience in Data Engineering / Big Data / Analytics. 4+ years of hands-on Databricks experience preferred. Strong experience designing enterprise-scale data platforms. Demonstrated experience leading technical projects and mentoring engineers. Strong communication and stakeholder-management skills.

For applications and inquiries,

Jobcon Logo Position Details

Posted:

Oct 12, 2026

Reference Number:

106075-38381

Employment:

Full-time

City:

Atlanta

Job Origin:

CIEPAL_ORGANIC_FEED

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Job Title: Senior Databricks Engineer
Location: 100% Remote
Interview Process: Video (2 Rounds)

Job Description: Lead/Senior Databricks Data Engineer Position: Lead Databricks Data Engineer / Databricks Architect Experience: 10+ Years Location: Remote

Job Summary We are looking for an experienced Lead Databricks Data Engineer / Databricks Architect with 10+ years of overall experience in Data Engineering and strong hands-on expertise in Databricks, Apache Spark, PySpark, SQL, Delta Lake, and cloud-based data platforms. The candidate will be responsible for designing and implementing scalable Lakehouse architectures, enterprise data pipelines, data integration solutions, governance frameworks, and high-performance analytics platforms using Databricks.

Key Responsibilities
  • Design and develop scalable data engineering solutions using Databricks and Lakehouse architecture.
  • Build robust ETL/ELT pipelines using PySpark, Spark SQL, Python, and SQL.
  • Design and implement Bronze, Silver, and Gold/Medallion architecture.
  • Develop and optimize Delta Lake tables, including MERGE, schema evolution, Change Data Feed, and incremental processing.
  • Build batch and real-time/streaming pipelines using Structured Streaming, Auto Loader, and Lakeflow.
  • Develop and manage Databricks Jobs/Workflows for pipeline orchestration, scheduling, dependencies, retries, and monitoring.
  • Implement enterprise data governance using Unity Catalog, including access control, data lineage, auditing, catalogs, schemas, and external locations.
  • Perform Spark and Databricks performance tuning, including cluster configuration, partitioning, caching, query optimization, Photon, and workload optimization.
  • Design data models supporting Data Warehousing, BI, Analytics, and AI/ML workloads.
  • Integrate Databricks with cloud platforms such as AWS, Azure, or Google Cloud Platform.
  • Work with cloud services such as AWS S3, Azure ADLS Gen2, Azure Data Factory, AWS Glue, Synapse, Event Hubs/Kafka/Kinesis, as applicable.
  • Implement CI/CD pipelines using Git, Azure DevOps/GitHub/Jenkins and Databricks deployment capabilities.
  • Work with Terraform/IaC for infrastructure provisioning and automation.
  • Troubleshoot production pipeline failures, performance issues, data-quality problems, and Spark/cluster issues.
  • Establish data quality, monitoring, logging, and observability practices.
  • Provide technical leadership, code reviews, architecture guidance, and mentorship to junior/mid-level engineers.
  • Collaborate with Data Architects, Data Scientists, Business Analysts, DevOps teams, and application teams.
Required Technical Skills
  • Databricks
  • Databricks Lakehouse Platform
  • Delta Lake
  • Unity Catalog
  • Databricks Workflows/Jobs
  • Lakeflow / Delta Live Tables
  • Auto Loader
  • Databricks SQL
  • Databricks notebooks
  • Databricks Asset Bundles
  • Photon
  • Cluster/workload optimization
  • Big Data
  • Apache Spark
  • PySpark
  • Spark SQL
  • Structured Streaming
  • Kafka
  • Batch and real-time data processing
  • Python
  • SQL
  • PySpark
  • Scala
  • AWS: S3, Glue, EMR, Lambda, Redshift, IAM, Kinesis
  • Azure: ADLS Gen2, ADF, Synapse, Azure DevOps, Event Hubs, Key Vault
  • Google Cloud Platform: GCS, BigQuery, Dataflow, Pub/Sub
  • ETL/ELT
  • Data Warehousing
  • Dimensional Modeling
  • Data Lake/Lakehouse
  • Medallion Architecture
  • CDC
  • Data Quality
  • Data Governance
  • Metadata and Data Lineage
  • Git
  • Azure DevOps / GitHub
  • Jenkins
  • Terraform
  • CI/CD automation
  • Infrastructure as Code
Preferred / Nice-to-Have Skills
  • MLflow
  • Databricks Machine Learning
  • Feature Store
  • Mosaic AI / GenAI
  • dbt
  • Apache Airflow
  • Power BI / Tableau
  • Delta Sharing
  • Lakehouse Federation
  • Liquid Clustering
  • Data security, PII masking

Qualifications

Bachelor's degree in Computer Science, Engineering, Information Technology, or related field. 10+ years of experience in Data Engineering / Big Data / Analytics. 4+ years of hands-on Databricks experience preferred. Strong experience designing enterprise-scale data platforms. Demonstrated experience leading technical projects and mentoring engineers. Strong communication and stakeholder-management skills.

For applications and inquiries,

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