Senior Data Engineer with Databricks Exp. - 100% Remote

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. Unity Catalog provides centralized governance, access control, lineage, and auditing across Databricks data and AI assets. ()

• 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

Programming

• Python

• SQL

• PySpark

• Scala good to have

Cloud Strong experience in at least one

• 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

Data Engineering

• ETL/ELT

• Data Warehousing

• Dimensional Modeling

• Data Lake/Lakehouse

• Medallion Architecture

• CDC

• Data Quality

• Data Governance

• Metadata and Data Lineage

DevOps / CI-CD

• 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 and PII masking

MLflow is particularly useful if the role touches ML/AI, as Databricks supports model tracking, lifecycle management, and deployment workflows alongside governed data. ()

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.