Databricks Engineer

Databricks Engineer

Data Lakehouse   |   Delta Lake   |   PySpark   |   MLflow   |   Unity Catalog

We are looking for a skilled and passionate Databricks Engineer to design, build, and optimize enterprise-scale data lakehouse solutions on the Databricks platform. The successful candidate will be responsible for creating Databricks pipeline delivering Financial Crime platforms covering Anti-Money Laundering (AML), Know Your Customer (KYC), Customer Risk Assessment (CRA), Sanctions Screening, Transaction Monitoring, Fraud Detection, and Regulatory Reporting

Position Details

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

Key Responsibilities

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 Qualifications

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.

Core Competencies

●     Deep technical depth in Databricks with the ability to architect and troubleshoot complex lakehouse systems.

●     Strong problem-solving skills — ability to diagnose pipeline failures, performance bottlenecks, and data quality issues.

●     Collaborative team player comfortable working with data engineers, ML engineers, and business stakeholders.

●     Excellent documentation and communication skills for technical and non-technical audiences.

●     Continuous learner — actively keeps pace with Databricks platform updates and the broader data/AI ecosystem.

●     Delivery-oriented mindset with experience in Agile/Scrum delivery environments.

What We Offer

●     Work on large-scale, production Databricks implementations for marquee enterprise clients.

●     Exposure to the full Databricks ecosystem — lakehouse, streaming, GenAI, and MLOps.

●     Databricks certification sponsorship and continuous learning support.

●     Competitive compensation, performance bonuses, and comprehensive benefits.

●     Hybrid/remote work flexibility and inclusive, high-performance team culture.

●     Direct collaboration with Databricks account teams and technical partners.