Senior Data Engineer

Description

We are looking for Senior Data Engineers to join our Data Platform team and build scalable, high-performance data platforms that power data processing, analytics, and downstream applications.

The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Apache Spark and Python Scala.

You will be responsible for designing, developing, and optimizing large-scale data pipelines while collaborating closely with cross-functional engineering teams to build reliable, production-grade data solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL and data processing pipelines for large-scale datasets.
• Build and optimize distributed data applications using Apache Spark and Python Scala.
• Develop reliable, high-performance data pipelines for batch and streaming workloads.
• Design and manage data workflows using Apache Airflow.
• Build and operate data workloads on AWS, with strong usage of Amazon S3 for large-scale data storage.
• Work with large datasets to ensure data quality, consistency, reliability, and performance.
• Collaborate with engineering, product, analytics, and other platform teams to deliver robust data solutions.
• Optimize data workflows for scalability, reliability, performance, and cost efficiency.
• Troubleshoot production issues, identify bottlenecks, and continuously improve platform performance.

Requirements

Candidates who demonstrate:

• 5+ years of experience in Data Engineering, Big Data Engineering, or a similar role.
• Strong hands-on experience with Apache Spark and Scala.
• Experience designing, building, and maintaining large-scale ETL pipelines.
• Strong hands-on experience with AWS, particularly Amazon S3.
• Hands-on experience with Apache Airflow for workflow orchestration and scheduling.
• Strong SQL skills and a solid understanding of distributed data processing concepts.
• Experience working with batch and/or streaming data pipelines.
• Excellent debugging, problem-solving, and performance optimization skills.
• Strong communication and collaboration skills.

Good to Have

• Experience with Databricks and the broader Databricks data platform.
• Familiarity with streaming technologies such as Apache Kafka.
• Experience working on large-scale data platforms handling high-volume data workloads.
• Exposure to additional AWS data services and cloud-native data architectures.

Description

We are looking for Senior Data Engineers to join our AdTech team and build scalable, high-performance data platforms that power advertising insights and analytics. The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Spark and Scala.

You will work on designing, developing, and optimizing large-scale data pipelines while collaborating closely with cross-functional engineering teams to build reliable, production-grade data solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL pipelines for large-scale data processing.
• Build and optimize distributed data applications using Spark and Scala.
• Develop reliable, high-performance data pipelines for batch and streaming workloads.
• Work with large datasets to ensure data quality, consistency, and performance.
• Collaborate with engineering, product, and analytics teams to deliver robust data solutions.
• Optimize data workflows for scalability, reliability, and cost efficiency.
• Deploy and manage data workloads in cloud and containerized environments.
• Troubleshoot production issues and continuously improve platform performance.

Requirements

Candidates who demonstrate:

• 5+ years of experience in Data Engineering or Big Data Engineering.
• Strong hands-on experience with Apache Spark and Scala.
• Experience building and maintaining ETL pipelines.
• Familiarity with Google Cloud Storage (GCS).
• Experience with Kubernetes (K8s).
• Strong SQL skills and understanding of distributed data processing.
• Excellent debugging, problem-solving, and performance optimization skills.
• Strong communication and collaboration skills.

Good to Have

• Experience with AWS and cloud-native data services.
• Familiarity with streaming technologies such as Kafka.
• Experience working on large-scale data platforms or AdTech systems.
• Exposure to orchestration tools such as Airflow.

Benefits

• Best-in-class salary: We hire strong talent and compensate accordingly.
• Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
• Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
• High-impact work: Build AI-first systems and products used at scale by global clients.

About Us

Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.

Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.

Job Title : Senior Data Engineer – Databricks

Experience : 14 to 20 Years

Location : HSR Layout, Bangalore

Work Mode : Hybrid – 3 Days WFO

Shift : 11:30 AM – 07:30 PM IST

Positions : 2

Notice Period : Immediate Joiners Only

Interview : 1 Technical Round + 2 Client Rounds

Role Overview :

We are looking for a Senior Data Engineer to build and lead enterprise-scale data platforms for a Switzerland-based commodity client.

The role requires a strong hands-on Data Engineering professional with expertise in Databricks, PySpark, Python, SQL, and AWS, along with technical leadership and stakeholder management experience.

Must-Have Skills :

• 14 to 20 years of Data Engineering experience
• Databricks & Apache Spark / PySpark
• Python & SQL
• AWS Cloud
• Lakehouse Architecture
• ETL / ELT & Distributed Data Processing
• Batch & Streaming Pipelines
• Data Pipeline Optimization & Data Modeling
• CDC & Incremental Processing
• Git, CI/CD & Testing
• Data Quality, Monitoring & Observability
• Technical Leadership & Stakeholder Management

Key Responsibilities :

• Design and build scalable data pipelines using Databricks, PySpark, Python, SQL, and AWS.
• Own data products from design through production.
• Develop batch / streaming pipelines and reusable ETL / ELT frameworks.
• Optimize pipelines for performance, scalability, reliability, and cost.
• Design scalable data architectures and data models.
• Implement data quality, monitoring, lineage, and CI/CD practices.
• Lead technical discussions and mentor engineering teams.
• Collaborate with business stakeholders, architects, product owners, and engineering teams.
• Remain hands-on while providing technical leadership.

Ideal Candidate :

A 14 to 20 years experienced, hands-on Data Engineering leader with strong Databricks + PySpark + AWS expertise, excellent communication, stakeholder management, and experience delivering enterprise-scale data platforms.

🔴 Super Urgent : Only Bangalore-based immediate joiners.

1st virtual , 2nd round F2F

Python pyspark, SQL, data engineer

5+yrs

9+yrs

Bangalore/Hyderabad

immediate to 15days.

role: data engineer

Python pyspark, SQL, data engineer

5+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.
• 
  

🚨 WE ARE HIRING – SENIOR DATA ENGINEER | BANGALORE 🚨

Looking for experienced Senior Data Engineers with strong expertise in PySpark, Oracle SQL/PLSQL, and Data Modeling!

🔹 Role: Senior Data Engineer

🔹 Experience: 7+ Years

🔹 Location: Bangalore

🔹 CTC: Up to 26 LPA

🔹 Notice Period: Immediate to 10 Days Preferred

💻 Mandatory Skills

✅ PySpark

✅ Oracle SQL / PL/SQL

✅ Data Modeling & Design / Modernization

✅ Python

✅ ETL

✅ Data Pipelines

⚙️ Good to Have / Ecosystem Skills

✅ Kafka

✅ Hadoop

✅ AWS / Azure / GCP

✅ Git

✅ JIRA

📌 Key Responsibilities

• Develop and maintain scalable data pipelines using PySpark

• Work extensively with Oracle SQL/PLSQL for data processing and transformation

• Design and implement data models and data architecture

• Work on data design and modernization initiatives

• Develop and optimize ETL processes and data pipelines

• Handle large volumes of data using PySpark and Python

• Work with technologies such as Kafka, Hadoop, and Cloud platforms

• Collaborate with cross-functional teams to deliver scalable data solutions

• Use Git and JIRA for version control and project tracking

📩 Interested candidates can share their updated CV along with:

Total Experience:

Relevant PySpark Experience:

Relevant Oracle SQL/PLSQL Experience:

Data Modeling Experience:

Current Location:

Notice Period:

Current CTC:

Expected CTC:

#Hiring #DataEngineer #SeniorDataEngineer #PySpark #Oracle #PLSQL #DataModeling #Python #ETL #DataPipelines #Kafka #Hadoop #AWS #Azure #GCP #BangaloreJobs #ITJobs #TechJobs #ImmediateJoiners

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.

Job Title : Senior Data Engineer – GCP & Apache Spark

Job Details :

• Number of Positions : 3
• Employment Type : Contract-to-Hire (C2H)
• Experience Required : 5 to 8 Years
• Location : Manyata Tech Park, Bengaluru
• Work Mode : Hybrid – 3 Days in Office per Week
• Preferred Notice Period : Immediate to 15–20 Days
• Project Duration : Initially 6–8 months, with a strong possibility of extension for long-term projects
• Relocation : Candidates from outside Bengaluru can apply if they are willing to relocate.

Role Overview :

We are looking for experienced Senior Data Engineers with strong expertise in Google Cloud Platform (GCP), Apache Spark, BigQuery, and SQL to design, develop, and maintain scalable data pipelines and distributed data processing solutions.

The ideal candidate should have hands-on experience with cloud-based data engineering, end-to-end pipeline architecture, system design, performance optimization, debugging, and CI/CD practices.

Mandatory skills :

Apache Spark (PySpark / Scala), SQL, GCP, BigQuery, GCP Dataproc, Data Pipelines, System Design, CI/CD, Debugging & Problem-Solving.

Key Responsibilities :

• Design, develop, and maintain scalable data pipelines using Apache Spark, PySpark, and/or Scala.
• Build and optimize batch and distributed data processing workflows on GCP.
• Work extensively with BigQuery for data transformation, validation, analytics, and query optimization.
• Develop and maintain data workflows using GCP services, including Dataproc, BigQuery, and Cloud Storage.
• Design end-to-end data pipeline architectures with a focus on scalability, reliability, and performance.
• Write and optimize complex SQL queries for large-scale datasets.
• Troubleshoot and resolve data processing issues, pipeline failures, and performance bottlenecks.
• Implement CI/CD practices for data engineering workflows, testing, and deployments.
• Monitor pipeline performance and identify opportunities for continuous improvement.
• Collaborate with cross-functional teams to understand requirements and deliver reliable data solutions.
• Participate in code reviews, testing, production deployments, and production support.

Mandatory Skills :

• Strong hands-on experience with Apache Spark, including PySpark and/or Scala.
• Strong SQL skills and experience working with large-scale datasets.
• Hands-on experience with Google Cloud Platform (GCP).
• Practical experience with BigQuery and data warehousing concepts.
• Working knowledge of GCP Dataproc.
• Strong understanding of data pipelines, end-to-end workflows, and system design.
• Practical experience with CI/CD and deployment processes.
• Excellent debugging, troubleshooting, and problem-solving skills.

Preferred Skills :

• Experience with Apache Airflow, Google Cloud Composer, or similar orchestration tools.
• Knowledge of Kafka or other streaming and messaging technologies.
• Proficiency in Python for data engineering and automation.
• Experience with Git-based development workflows.
• Knowledge of Linux/Unix environments and shell scripting.
• Familiarity with monitoring, logging, data quality, and governance practices.

Required Qualifications :

• 5 to 8 years of experience in Data Engineering, Big Data Engineering, or a related field.
• Proven experience developing and maintaining scalable data pipelines.
• Strong understanding of distributed data processing and cloud-based data architecture.
• Ability to troubleshoot complex technical issues and optimize pipeline performance.
• Strong communication, collaboration, and ownership skills.

Interview Process :

Round 1 : AI-Based Interview

• Candidates must complete the AI interview as soon as possible after receiving the invitation.

Round 2 : Virtual Interview Drive

• Interviews are planned for the upcoming drive; exact dates will be communicated separately.

Round 3 : Online Client Interview

• Shortlisted candidates will attend the online interview.
• Candidates must be available throughout the day on the scheduled interview date.

1st virtual , 2nd round F2F -

Python pyspark, SQL, data engineer

5+yrs

Bang/hyderabad

immediate to 15days.