Senior Data Engineer – Databricks

About the Role

We are looking for a Senior Data Engineer with strong hands-on expertise in Databricks, Python, PySpark, and SQL to build scalable, high-performance data engineering solutions. You’ll architect and develop large scale, high-performance data pipelines capable of handling massive real-time and batch data volumes across multiple business systems. Databricks is the core enterprise data and processing platform for this role. You will also use Apache Airflow for workflow orchestration and dbt for ELT transformations, and will contribute to designing reliable, secure, and governed data platforms that enable analytics, reporting, and AI-driven use cases.

Key Responsibilities

• Design and implement large-scale data pipelines using Python/PySpark, Databricks, and Microsoft Fabric.
• Develop and optimize data processing workloads in Databricks using PySpark and Spark SQL, with a strong focus on scalability, reliability, performance, and maintainability.
• Develop and maintain dbt models including layered architecture, incremental models, snapshots, macros, testing, and documentation.
• Design, develop, and maintain Apache Airflow DAGs for orchestrating reliable, scalable, and observable data pipelines.
• Design and implement data quality, observability, and governance frameworks, including automated testing, monitoring, lineage, access control, and data privacy standards.
• Partner with analytics, product, and business stakeholders to turn requirements into trustworthy datasets, and raise the engineering bar through design discussions, code reviews, and mentoring junior engineers.

Required Skills

• Strong expertise in Python for developing scalable, modular, and production-ready data engineering applications.
• Strong expertise in PySpark, including DataFrame API, Spark SQL, Structured Streaming, partitioning strategies, joins, caching, handling data skew, and Spark performance optimization.
• Strong hands-on experience with Databricks for data ingestion, transformation, processing, and optimization, including Delta Lake, Unity Catalog, Databricks Workflows, notebooks, jobs, and Databricks-native data engineering capabilities.
• Strong experience in Databricks/Spark performance tuning, including query and job optimization, partitioning, file sizing, caching, join optimization, handling data skew, and efficient use of compute resources.
• Hands-on experience with Delta Lake, including transactional data processing, schema management, incremental data processing, and reliable batch and streaming data pipelines.
• Hands-on experience in developing dbt projects using layered architecture, incremental models, snapshots, macros/Jinja, testing, documentation, and deployment best practices.
• Expertise in advanced SQL and data modelling — dimensional modeling, slowly changing dimensions, schema evolution, and query optimization.
• Hands-on experience in developing and managing Apache Airflow DAGs, scheduling workflows, dependency management, retries, backfills, and operational monitoring.
• Hands-on experience with at least one major cloud platform (AWS, Azure or GCP).
• Strong problem-solving skills and the ability to work independently with business and analytics stakeholders.

Nice to Have

• Hands-on exposure to Microsoft Fabric for data integration and analytics.
• Experience using AI coding assistants (e.g. Claude Code, GitHub Copilot) as part of a development workflow.
• Familiarity with modern DevOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and containerization (Docker/Kubernetes).
• Domain expertise in financial services.

SENIOR DATA ENGINEER – DATABRICKS & SNOWFLAKE

Experience: 7+ Years

Location: Remote

Employment Type: Full-Time

Budget: Up to ₹30 LPA

Time Zone: US Eastern Time (ET)

Shift: US ET Night Shift

Job Description

We are looking for an experienced Senior Data Engineer with strong expertise in Databricks, Snowflake, Apache Spark, and AWS to design, develop, and maintain scalable data engineering solutions.

The candidate will work on integrating data from multiple sources, building modern data pipelines, developing enterprise data models, and preparing data platforms for AI/ML applications.

Experience in healthcare, scientific data, or the histopathology domain will be an added advantage.

Key Responsibilities

• Design, build, and maintain scalable data pipelines to acquire, integrate, and manage data from diverse sources such as laboratory systems, MES, clinical supply, quality systems, and external partners.
• Develop and optimize data workflows for structured and unstructured data using Python, PySpark, R, SQL, Databricks, and Snowflake.
• Design and maintain enterprise-level data models and data repositories.
• Perform ETL development, data warehousing, database design, and data integration.
• Ensure data quality, versioning, traceability, and consistency to support AI/ML readiness.
• Collaborate with stakeholders and technical teams to translate business requirements into technical solutions.
• Manage multiple projects while ensuring scalability, performance, and reliability.

Required Skills

• 7+ years of experience in Data Engineering.
• Strong hands-on experience with Databricks and Snowflake.
• Excellent knowledge of Apache Spark / PySpark.
• Strong experience with AWS cloud services and AWS RDS.
• Experience with Oracle and Microsoft SQL Server (MSSQL).
• Proficiency in Python, R, and SQL.
• Strong understanding of ETL, Data Warehousing (DWH), data modeling, and database design.
• Experience with dbt and cloud-based data architectures.
• Knowledge of NoSQL and graph databases.
• Strong analytical, problem-solving, and stakeholder-management skills.
• Good English communication skills (B2 level or above).
• Willingness to work in the US Eastern Time (ET) night shift.

Preferred / Good to Have

• Experience in histopathology, healthcare, life sciences, or scientific data environments.
• Knowledge of CDISC, HL7, FHIR, OMOP, DICOM, or other regulated data standards.
• Experience handling high-dimensional data, including imaging and sensor data.
• Familiarity with MLOps and machine learning model deployment workflows.
• Knowledge of Manufacturing Execution Systems (MES) and Laboratory Information Management Systems (LIMS).
• Exposure to knowledge graphs and ontology-driven architectures.

Education

Bachelor's degree in Engineering, Data Science, Life Sciences, Computer Science, or a related field. A master's degree is preferred.

Interested candidates can apply with their updated resumes.

Job Summary

We are looking for an experienced Data Engineer to build and optimize scalable data pipelines and AWS-based data platforms.

Key Responsibilities

• Develop and maintain data pipelines using Python and PySpark.
• Design and implement data lakehouse solutions on AWS.
• Build and manage data workflows using AWS Glue, EMR, ECS, and Step Functions.
• Work with DynamoDB and other data storage technologies.
• Monitor data pipelines and ensure data quality, performance, and reliability.
• Collaborate with stakeholders and engineering teams to deliver data solutions.

Mandatory Skills

• 5+ years of overall professional experience.
• 3+ years of experience in data platforms and big data technologies.
• Strong proficiency in Python and PySpark.
• Hands-on experience with AWS EMR, ECS, Glue, Step Functions, and DynamoDB.
• Experience designing and implementing AWS data lakehouse architectures.

Good to Have

• Apache Spark and Apache Iceberg.
• AWS Lambda, EventBridge, Kinesis, and Redshift.
• Experience with real-time data pipelines and event-driven architectures.

Responsibilities and JD

Job Description: We are looking for a Senior Developer with strong expertise in PySpark, Databricks, and Snowflake to build scalable data engineering solutions and enterprise data platforms.

Key Responsibilities:

1. Design, develop, and maintain ETL/ELT pipelines using PySpark, Databricks, and Snowflake.
2. Develop batch and real-time data processing solutions for structured and semi-structured data.
3. Build and optimize Databricks notebooks, workflows, and Delta Lake solutions.
4. Design and implement Snowflake databases, schemas, views, stored procedures, tasks, and streams.
5. Develop scalable data models, data marts, and data warehouse solutions.
6. Optimize PySpark jobs, Databricks workloads, and Snowflake queries for performance and cost efficiency.
7. Implement data quality, validation, governance, and security controls.
8. Collaborate with business stakeholders, architects, and cross-functional teams to deliver data solutions.
9. Manage source control and CI/CD deployments using Git and Azure DevOps.
10. Troubleshoot production issues, perform root cause analysis, and ensure pipeline reliability.
11. Mentor junior team members and participate in code reviews and technical design discussions.

Required Skills: PySpark, Databricks, Snowflake, Python, SQL.

Experience: 5+ years of Data Engineering experience with strong hands-on expertise in PySpark, Databricks, and Snowflake.

role: data engineer

Python pyspark, SQL, data engineer

5+yrs

Bangalore/Hyderabad

immediate to 15days.

Job Description:

We are seeking a skilled Senior Data Engineer with expertise in Databricks to join our dynamic data team. The ideal candidate will design, build, and maintain scalable data pipelines and architectures to support our organization's data-driven initiatives and leverage Databricks to process large-scale datasets, optimize data workflows, enable advanced analytics and machine learning, and integrate Power BI for data visualization and reporting.

Key Responsibilities

·        5-8 years of professional work experience in a relevant field

·        Proficient in Microsoft Fabric platform, Azure Databricks, ADF, Delta Lake, SQL Data Warehouse, Unity Catalog.

·        Good Experience on Microsoft Dynamics 365

·        Experience/ prior knowledge on semi structure data and Structured Streaming, Azure synapse, data lake, data warehouse.

·        Proficient in creating Azure Data Factory pipelines for ETL/ELT processing; copy activity, custom Azure development etc.

·        Good knowledge of SQL and Python for data manipulation, transformation, and analysis

·        Understand business requirements to set functional specifications for reporting applications

• Data Pipeline Development: Design, develop, and maintain robust, scalable data pipelines using Databricks, Apache Spark, and other cloud-based technologies.
• Data Integration: Ingest, transform, and integrate data from diverse sources, including APIs, databases, streaming platforms, and third-party systems, into Databricks for analytics and reporting.
• Power BI Integration: Develop and optimize data models and datasets in Databricks for use in Power BI, ensuring efficient data connections and high-quality visualizations.
• Performance Optimization: Optimize data workflows, API calls, and queries on Databricks and Power BI for performance, cost-efficiency, and scalability.
• Data Modeling: Build and maintain data models to support business requirements, ensuring data quality, consistency, and accessibility for analytics and reporting.
• Cloud Integration: Implement data solutions on cloud platforms (e.g., AWS, Azure, GCP) integrated with Databricks, Power BI, and API ecosystems.
• Security & Compliance: Implement data governance, security, and compliance best practices within Databricks, Power BI, and API environments.
• Technical Skills:
• Proficiency in Databricks, including Delta Lake, Spark SQL.
• Strong programming skills in Python, Scala, or Java.
• Experience with Apache Spark for big data processing.
• Knowledge of SQL for querying and transforming data.
• Proficiency in Power BI for creating data models, DAX queries, and interactive dashboards.

Preferred Qualifications

• Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional).

Experience with real-time data processing and streaming

1st virtual , 2nd round F2F

Python pyspark, SQL, data engineer

5+yrs

9+yrs

Bangalore/Hyderabad

immediate to 15days.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines using Python and PySpark.
• Work extensively with Databricks and Delta Lake, including Delta tables, MERGE operations, schema evolution, Time Travel, and data optimization.
• Develop, schedule, and monitor Apache Airflow DAGs for workflow orchestration and dependency management.
• Work with Kubernetes for containerized application deployment, orchestration, monitoring, and troubleshooting.
• Develop reusable and optimized Python code following software engineering best practices.
• Optimize Spark jobs for performance, scalability, and cost efficiency.
• Implement data quality checks, error handling, logging, and monitoring mechanisms.
• Use Git for version control, branching, pull requests, code reviews, and collaborative development.
• Troubleshoot pipeline failures, investigate data issues, and implement reliable solutions.
• Collaborate with cross-functional teams to understand business requirements and deliver efficient data solutions.

Required Technical Skills

• Python: Strong programming skills, including data manipulation, exception handling, modular programming, and debugging.
• PySpark: Hands-on experience with Spark DataFrames, Spark SQL, joins, transformations, partitioning, and performance tuning.
• Databricks: Strong understanding of Databricks architecture, notebooks, workflows, compute management, and job execution.
• Delta Lake: Experience with Delta tables, ACID transactions, MERGE/UPSERT, schema enforcement, schema evolution, VACUUM, and Time Travel.
• Apache Airflow: Experience developing DAGs, managing task dependencies, configuring retries, scheduling workflows, and troubleshooting failures.
• Kubernetes: Practical knowledge of pods, deployments, services, ConfigMaps, Secrets, resource management, and troubleshooting containerized workloads.
• Git: Proficiency in branching strategies, pull requests, merging, and resolving conflicts.
• SQL: Strong SQL skills, including complex joins, CTEs, window functions, and query optimization.

Good to Have

• Experience with Docker and containerization best practices.
• Experience with Databricks Unity Catalog and data governance.
• Knowledge of CI/CD pipelines and automated deployments.
• Experience deploying and managing Airflow workloads on Kubernetes.
• Familiarity with Kubernetes monitoring, autoscaling, and resource optimization.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Understanding of data lakehouse architecture and data modeling.
• Experience with batch and incremental data processing.
• Familiarity with data quality frameworks and automated testing.

Preferred Candidate Profile

• Strong analytical and problem-solving skills.
• Ability to independently design, develop, deploy, and troubleshoot data pipelines.
• Good understanding of distributed data processing and container orchestration.
• Experience working in an Agile development environment.
• Strong communication and collaboration skills.
• 
  

1st virtual , 2nd round F2F -

Python pyspark, SQL, data engineer

5+yrs

Bang/hyderabad

immediate to 15days.

Technical Stack

- PySpark

- Databricks

- Microsoft Azure

- SQL

- Kubernetes

Key Responsibilities & Expectations

- Must be self-driven, proactive, and capable of independently delivering high-quality solutions.

- Demonstrate strong ownership and accountability for assigned tasks and deliverables.

- Ability to work with minimal supervision while consistently meeting project objectives and quality standards.