Senior Data Engineer with Databricks Exp. - 100% Remote

Job Title: Senior Databricks Engineer

Location: 100% Remote

Interview Process: Video (2 Rounds)

Job Description:

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.