The Role
Leads enterprise-scale data engineering projects using Databricks, Spark, PySpark, SQL, and Delta Lake across Azure and AWS. Designs Lakehouse architectures and batch or real-time pipelines, manages end-to-end delivery, optimizes performance and infrastructure costs, implements governance and CI/CD, and ensures security, quality, monitoring, and operational reliability. The role also involves client engagement, solution workshops, architecture reviews, stakeholder management, technical leadership, and mentoring data engineers.
Summary Generated by Built In
Job Title: Senior Databricks Engineer
Experience Required: 8+ Years
Role Summary
We are seeking an experienced Senior Databricks Developer with 8+ years of experience in Data Engineering, Big Data, and Cloud Analytics solutions, including extensive hands-onexpertise in Databricks, Apache Spark, PySpark, SQL, and Delta Lake. The ideal candidate will be responsible for leading end-to-end data engineering projects, engaging directly with clients and business stakeholders, mentoring development teams, and delivering scalable, secure, and high-performance data platforms across Azure and AWS environments.
This role requires strong technical leadership, solution design capabilities, stakeholder management skills, and the ability to drive data transformation initiatives from requirements gathering through deployment and production support.
Technical Expertise
Databricks (PySpark, SQL, Notebooks, Workflows) , Apache Spark , Delta Lake , Python, Azure
Data Factory (ADF) ,Azure Synapse Analytics ,AWS S3, Glue, Lambda , Unity Catalog
MLflow ,Databricks Job Clusters ,CI/CD Pipelines ,GitLab / GitHub ,Data Governance & Data
Quality Frameworks, Power BI / Tableau , Data Cataloging ,Lakehouse Architecture ,Medallion
Architecture, Performance Tuning & Optimization
Key Responsibilities
• Lead the design, development, and implementation of enterprise-scale data engineering
solutions using Databricks, PySpark, SQL, and Delta Lake.
• Own end-to-end project delivery, including requirement gathering, solution design,
development, testing, deployment, and production support.
• Collaborate directly with clients, business stakeholders, architects, and product owners to
understand business requirements and translate them into scalable technical solutions.
• Conduct client discussions, solution workshops, effort estimations, technical presentations,
and architecture reviews.
• Design and implement scalable Lakehouse architectures and data platforms across Azure and
AWS cloud environments.
• Lead the development of high-performance ETL/ELT pipelines supporting both batch and
real-time data processing workloads.
• Drive best practices around coding standards, architecture governance, version control, CI/CD
implementation, and operational excellence.
• Architect and optimize Databricks Workflows, Job Clusters, Delta Tables, and Spark
applications to maximize performance and minimize infrastructure costs.
• Implement data governance frameworks using Unity Catalog, ensuring proper access controls,
lineage tracking, metadata management, and compliance standards.
• Mentor and guide junior and mid-level data engineers through code reviews, technical
coaching, and knowledge-sharing initiatives.
• Lead technical teams and coordinate project activities to ensure timely and successful project
delivery.
• Collaborate with Data Scientists, BI Teams, and Analytics stakeholders to enable advanced
analytics and machine learning use cases.
• Drive automation initiatives through Infrastructure as Code (IaC), DevOps practices, and
CI/CD pipelines.
• Establish monitoring, observability, and performance tracking frameworks for data platforms
and pipelines.
• Ensure security, compliance, data quality, and operational reliability across enterprise data
ecosystems.
• Prepare and maintain technical architecture documents, design specifications, implementation
guides, and operational runbooks.
Requirements
• 8+ years of experience in Data Engineering, Big Data, and Analytics platforms.
• Minimum 5+ years of hands-on experience working with Databricks, Apache Spark, PySpark,
and SQL.
• Strong expertise in designing and implementing enterprise-scale Data Lake, Lakehouse, and
Data Warehouse solutions.
• Deep understanding of Spark internals, cluster management, performance tuning, partitioning
strategies, and resource optimization.
• Extensive experience working with Delta Lake, schema evolution, ACID transactions, and
large-scale distributed data processing.
• Hands-on experience integrating Databricks with Azure (ADF, Synapse) and/or AWS (S3,
Glue, Lambda) services.
• Proven experience handling end-to-end project delivery and managing technical engagements
with clients and stakeholders.
• Experience leading development teams and mentoring engineers in enterprise environments.
• Strong understanding of data modeling, dimensional modeling, Medallion Architecture, and
modern data platform design principles.
• Expertise in implementing CI/CD pipelines, DevOps practices, and automated deployment
strategies.
• Experience with Unity Catalog or equivalent governance platforms.
• Excellent communication, stakeholder management, presentation, and client-facing skills.
• Ability to lead technical discussions, architecture reviews, and solution design workshops.
• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a
related field.
Information Security & ISO 27001 Compliance
Security is at the heart of everything we do. In this role, you will be strictly required to uphold our Information Security Management System (ISMS) policies in alignment with ISO/IEC 27001 standards. Responsibilities include safeguarding sensitive asset data, completing mandatory security awareness training, reporting potential security incidents or vulnerabilities immediately, and ensuring that daily operations comply with our rigorous data protection protocols.
Skills Required
- 8+ years of experience in data engineering, Big Data, and analytics platforms
- 5+ years of hands-on experience with Databricks, Apache Spark, PySpark, and SQL
- Experience designing and implementing enterprise-scale data lake, Lakehouse, and data warehouse solutions
- Deep understanding of Spark internals, cluster management, performance tuning, partitioning strategies, and resource optimization
- Extensive experience with Delta Lake, schema evolution, ACID transactions, and large-scale distributed data processing
- Hands-on experience integrating Databricks with Azure Data Factory, Azure Synapse, AWS S3, AWS Glue, and/or AWS Lambda
- Experience with end-to-end project delivery and technical client and stakeholder engagements
- Experience leading development teams and mentoring engineers in enterprise environments
- Strong understanding of data modeling, dimensional modeling, Medallion Architecture, and modern data platform design principles
- Expertise in CI/CD pipelines, DevOps practices, and automated deployment strategies
- Experience with Unity Catalog or equivalent data governance platforms
- Excellent communication, stakeholder management, presentation, and client-facing skills
- Ability to lead technical discussions, architecture reviews, and solution design workshops
- Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related field
- Compliance with ISO/IEC 27001 information security and data protection policies
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The Company
What We Do
DataBeat helps enterprises and publishers navigate data analytics and ad technology through advanced analytics, programmatic advertising, and ad-operations support. Its capabilities include big-data engineering, data visualization, and yield optimization, helping clients turn data into actionable, ROI-boosting insights and improve advertising performance. The Princeton-based company provides media, advertising, and analytics solutions across complex, data-driven digital ecosystems for business customers and enterprise teams worldwide.
