Job Title : Databricks Engineer

Department : Client Delivery / Data & AI Engineering

Experience Required : 8 - 10 Years (overall) | 5+ Years Databricks hands-on

Employment Type : Full-Time

Location : Hybrid (as per business requirement)

Reporting To : Delivery Manager / Technical Project Manager

Notice Period : Immediate - 10 Days

Job Description :

Databricks Platform Engineering :

- Design, build, and maintain Databricks workspaces, clusters, and compute pools across dev/test/prod environments.

- Configure and manage Databricks Unity Catalog for data governance, access control, fine-grained permissions, and data lineage.

- Optimize cluster configurations instance types, auto-scaling policies, spot/preemptible nodes for cost and performance.

- Implement workspace-level best practices: folder structures, access controls, secret management (Databricks Secrets / Azure Key Vault / AWS Secrets Manager).

- Manage Databricks jobs, workflows, and multi-task job orchestration with dependency management.

Delta Lake & Lakehouse Architecture :

- Design and implement Delta Lake tables with appropriate partitioning, Z-ordering, and file compaction (OPTIMIZE / VACUUM).

- Build Medallion Architecture (Bronze / Silver / Gold) layers for structured data lake organization.

- Implement Delta Live Tables (DLT) pipelines for declarative, reliable ETL/ELT with built-in data quality expectations.

- Manage schema evolution, table versioning, time travel, and Change Data Feed (CDF) for incremental processing.

- Design data lakehouse patterns integrating Delta Lake with external systems (Kafka, ADLS, S3, GCS).

Data Pipeline Development (PySpark / SQL) :

- Develop scalable batch and streaming data pipelines using PySpark, Spark SQL, and Delta Lake.

- Build structured streaming pipelines for real-time ingestion from Kafka, Event Hubs, and Kinesis into Delta tables.

- Write optimized PySpark transformations leveraging broadcast joins, adaptive query execution (AQE), and dynamic partition pruning.

- Create reusable transformation libraries, utility frameworks, and pipeline templates for team productivity.

- Implement robust error handling, retry logic, and dead-letter queue patterns in production pipelines.

MLflow & AI/ML Workloads :

- Set up and manage MLflow tracking servers, experiment registries, and model lifecycle management on Databricks.

- Support data scientists and ML engineers in deploying model training and inference workloads on Databricks clusters and GPU instances.

- Build feature engineering pipelines using Databricks Feature Store for reusable, versioned ML features.

- Enable GenAI workloads LLM fine-tuning, RAG pipeline development, and vector search (Databricks Vector Search / Mosaic AI).

- Implement MLOps practices: model versioning, A/B testing, model serving via Databricks Model Serving endpoints.

Cloud Integration & DevOps :

- Integrate Databricks with cloud-native services : Azure Data Lake Storage (ADLS).

- Build and maintain CI/CD pipelines for Databricks notebooks and jobs using Azure DevOps, GitHub Actions, or GitLab CI.

- Implement Databricks Asset Bundles (DABs) or Terraform for infrastructure-as-code (IaC) deployment of Databricks resources.

- Manage data ingestion using Auto Loader, COPY INTO, and partner integrations (Fivetran, dbt, Airbyte).

- Monitor pipeline health, cluster utilization, and costs using Databricks system tables and cloud cost management tools.

Governance, Security & Optimization :

- Implement row-level security, column masking, and dynamic data views using Unity Catalog policies.

- Ensure data quality enforcement using Delta Live Tables expectations and Great Expectations integrations.

- Conduct performance tuning query plan analysis, caching strategies, Photon engine enablement.

- Maintain data cataloging, metadata management, and data lineage tracking within Unity Catalog.

- Document architecture decisions, runbooks, and operational guides for Databricks workloads.

Required Qualification :

Education :

- Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or related field.

Experience :

- 8+ years of total experience in data engineering or software engineering.

- 3+ years of dedicated hands-on experience with the Databricks platform in production environments.

- Strong background in big data engineering, cloud data platforms, and distributed computing.

Databricks Platform :

- Deep expertise in Databricks Workspaces, Clusters, Jobs, Workflows, and Repos.

- Proficiency with Unity Catalog metastore setup, catalog/schema/table management, access controls, and data lineage.

- Hands-on experience with Delta Live Tables (DLT) pipeline development, expectations, and monitoring.

- Strong command of Delta Lake internals transaction log, ACID guarantees, file layout, and optimization techniques.

- Experience with Databricks SQL Warehouses, SQL Analytics, and dashboard creation.

- Knowledge of Databricks Photon engine, serverless compute, and cost optimization strategies.

PySpark & SQL :

- 4+ years of PySpark development DataFrames, Datasets, Spark SQL, RDD operations.

- Expert-level SQL window functions, lateral joins, CTEs, recursive queries, and analytical functions.

- Experience with Spark performance tuning AQE, query plans (EXPLAIN), partitioning, and caching.

- Proficiency with Python for pipeline development, utilities, and automation.

Cloud Platforms :

- Hands-on experience with at least one : Azure (ADLS Gen2, ADF, Azure Databricks), AWS (S3, EMR, Glue, AWS Databricks), or GCP (GCS, BigQuery, Dataproc).

- Experience with cloud networking for Databricks: VNet/VPC injection, private endpoints, and firewall configurations.

- Familiarity with IAM roles, managed identities, and service principal authentication for Databricks.

MLflow & ML Engineering (Nice to Have) :

- Working knowledge of MLflow experiment tracking, model registry, and deployment.

- Experience supporting ML pipelines on Databricks for training, evaluation, and serving.

- Exposure to Databricks Feature Store and Mosaic AI / GenAI capabilities.

Preferred Certifications :

- Databricks Certified Associate Developer for Apache Spark (PySpark or Scala).

- Databricks Certified Data Engineer Associate / Professional strongly preferred.

- Databricks Certified Machine Learning Associate / Professional.

- Azure Data Engineer Associate (DP-203) / AWS Data Analytics Specialty / GCP Professional Data Engineer.

- dbt Analytics Engineer Certification.

Preferred Qualifications :

- Experience with dbt (data build tool) for SQL-based transformation on Databricks SQL.

- Knowledge of Apache Kafka / Confluent for real-time streaming into Databricks.

- Familiarity with Terraform or Pulumi for Databricks infrastructure-as-code.

- Exposure to Apache Iceberg or Apache Hudi in addition to Delta Lake.

- Experience with BI tool integration : Power BI, Tableau, or Looker connected to Databricks SQL.

- Knowledge of data mesh principles and federated data governance at scale.

- Domain experience in BFSI, Healthcare, Retail, or Manufacturing data programs.