Lead Data Engineer at Innominds · Hyderabad · 10 - 15 years · Upto ₹36L / yr · Profitable · Posted 10 Apr 2025
We Help Our Customers Build Great Products.
Innominds is a trusted innovation acceleration partner focused on designing, developing and delivering technology solutions for specialized practices in Big Data & Analytics, Connected Devices, and Security, helping enterprises with their digital transformation initiatives. We built these practices on top of our foundational services of innovation, like UX/UI, application development and testing.
Over 1,000 people strong, we are a pioneer at the forefront of technology and engineering R& D, priding ourselves as being forward thinkers and anticipating market changes to help our clients stay relevant and competitive.
About the Role:
We are looking for a seasoned Data Engineering Lead to help shape and evolve our data platform. This role is both strategic and hands-on—requiring leadership of a team of data engineers while actively contributing to the design, development, and maintenance of robust data solutions.
Key Responsibilities:
- Lead and mentor a team of Data Engineers to deliver scalable and reliable data solutions
- Own the end-to-end architecture and development of data pipelines, data lakes, and warehouses
- Design and implement batch data processing frameworks to support large-scale analytics
- Define and enforce best practices in data modeling, data quality, and system performance
- Collaborate with cross-functional teams to understand data requirements and deliver insights
- Ensure smooth and secure data ingestion, transformation, and export processes
- Stay current with industry trends and apply them to drive improvements in the platform
Requirements
- Strong programming skills in Python
- Deep expertise in Apache Spark, Big Data ecosystems, and Airflow
- Hands-on experience with Azure cloud services and data engineering tools
- Strong understanding of data architecture, data modeling, and data governance practices
- Proven ability to design scalable data systems and enterprise-level solutions
- Strong analytical mindset and problem-solving skills
For our company to deliver world-class products and services, our business depends on recruiting and hiring the best and the brightest from around the globe. We are looking for the engineers, designers and creative problem solvers that stand out from the rest of the crowd but are also humble enough to continue learning and growing, are eager to tackle complex problems and are able to keep up with the demanding pace of our business. We are looking for YOU!

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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.
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.
1st virtual , 2nd round F2F
Python pyspark, SQL, data engineer
5+yrs
9+yrs
Bangalore/Hyderabad
immediate to 15days.
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:
- Design, develop, and maintain ETL/ELT pipelines using PySpark, Databricks, and Snowflake.
- Develop batch and real-time data processing solutions for structured and semi-structured data.
- Build and optimize Databricks notebooks, workflows, and Delta Lake solutions.
- Design and implement Snowflake databases, schemas, views, stored procedures, tasks, and streams.
- Develop scalable data models, data marts, and data warehouse solutions.
- Optimize PySpark jobs, Databricks workloads, and Snowflake queries for performance and cost efficiency.
- Implement data quality, validation, governance, and security controls.
- Collaborate with business stakeholders, architects, and cross-functional teams to deliver data solutions.
- Manage source control and CI/CD deployments using Git and Azure DevOps.
- Troubleshoot production issues, perform root cause analysis, and ensure pipeline reliability.
- 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.
Job Summary
We are looking for a skilled and experienced Data Engineer to join our growing data team. The ideal candidate will have strong expertise in Python, PySpark, Data Modeling, and Power BI, with hands-on experience in designing, developing, and optimizing scalable data solutions. The role requires working closely with business stakeholders, data architects, and analytics teams to build robust data pipelines and semantic models that enable data-driven decision-making.
Technical Skills
- Strong hands-on experience in Python and PySpark development.
- Expertise in building and optimizing Data Engineering solutions and ETL pipelines.
- Strong understanding of Data Modeling concepts (Star Schema, Snowflake Schema, Dimensional Modeling).
- Experience with Power BI Data Modeling and Semantic Layer development.
- Proficiency in DAX (Data Analysis Expressions).
- Experience designing and managing Semantic Models in Power BI.
- Strong SQL skills and experience working with large datasets.
- Knowledge of data warehousing concepts and best practices.
Preferred Skills
- Experience with cloud platforms such as Azure, AWS, or GCP.
- Exposure to modern data platforms like Databricks.
- Understanding of data governance and data quality frameworks.
Job Title : Tech Lead – Data Lake Platform
Number of Positions : 2
Experience : 7+ Years
Role Type : Technical Lead / Data Platform Lead
Domain : Data Engineering / Data Platform / AWS
About the Role :
We are looking for an experienced Tech Lead – Data Lake Platform to lead the design, development, and operations of an enterprise-scale AWS-based Data Lake Platform.
The platform will ingest data from multiple business systems, process it through structured data layers, and serve data for analytics, reporting, APIs, and operational applications.
As a Tech Lead, you will be responsible for setting the technical direction, leading data engineering teams, driving platform reliability and performance, and owning the platform's delivery, governance, and production support end to end.
Core Tech Stack :
AWS | S3 | EMR | Glue | Athena | Redshift | DMS | Lambda | RDS | IAM | Airflow | Spark/PySpark | SQL | Data Modeling | Hasura | GraphQL | DBT | PostgreSQL/Aurora | Kafka
Key Responsibilities :
- Own the overall Data Lake Platform architecture, covering data ingestion, staging, curated layers, consumption, analytics, and API/data serving.
- Lead the design and development of production-grade data pipelines using AWS Glue, EMR/Spark, Airflow, DBT, Athena, and Redshift.
- Design scalable batch and analytics pipelines with a focus on reliability, performance, data quality, and maintainability.
- Own the API/data-serving architecture from consumption data → RDS/PostgreSQL → Hasura GraphQL → Lambda/API Gateway.
- Drive improvements in platform stability, including orchestration failures, cluster sizing, pipeline SLAs, query performance, and production reliability.
- Design and implement appropriate AWS security, access control, IAM, PII handling, and data governance practices.
- Lead technical discussions, architecture decisions, code reviews, and engineering best practices.
- Mentor and guide data engineers while ensuring high-quality and scalable engineering delivery.
- Own production support, incident management, troubleshooting, and root-cause analysis (RCA) for critical data platform issues.
- Develop and maintain runbooks, operational procedures, monitoring, and incident response practices.
- Collaborate with business, product, application, and analytics teams to onboard new datasets and support reporting, API, and data consumption requirements.
- Ensure the platform meets defined availability, performance, security, data quality, and compliance requirements.
Must-Have Skills :
- 7+ years of experience in Data Engineering, Data Platform Engineering, or related areas, including experience in technical leadership or architecture.
- Strong hands-on experience with AWS Data Services, including:
- Amazon S3
- AWS EMR
- AWS Glue
- Amazon Athena
- Amazon Redshift
- AWS DMS
- AWS Lambda
- Amazon RDS
- AWS IAM
- Strong production experience with Apache Airflow.
- Strong hands-on experience with Apache Spark / PySpark.
- Strong SQL skills and experience with data modeling, including layered data architecture, data marts, and enterprise data models.
- Experience with Hasura or a similar GraphQL layer for PostgreSQL-based data/API serving.
- Strong understanding of data lake architecture and enterprise data platforms.
- Proven experience leading engineers and driving technical decisions.
- Hands-on experience with production support, troubleshooting, incident management, and RCA.
- Strong understanding of data platform performance, scalability, reliability, and SLA management.
Good-to-Have Skills :
- Experience with DBT and modern data transformation practices.
- Experience with lakehouse table formats on Amazon S3, such as Apache Iceberg or similar technologies.
- Strong knowledge of Amazon Redshift workload optimization, including :
- Distribution keys
- Sort keys
- Spectrum
- External tables
- Experience with Kafka or other streaming/data ingestion technologies.
- Experience with PostgreSQL / Amazon Aurora as a data-serving layer.
- Experience with Lambda and API Gateway for API-based data serving.
- Experience with enterprise data governance, data quality, security, and compliance.
- Experience in BFSI / Banking / Financial Services / Insurance domain.
Ideal Candidate :
The ideal candidate is a hands-on Data Platform / Data Engineering Lead who can operate at both the architecture and implementation level. You should be comfortable designing an AWS Data Lake from end to end, leading engineers, troubleshooting production issues, and working closely with business and application teams to deliver reliable data products.
Job Description – Azure Data Engineer
Role: Azure Data Engineer
Experience: 9+ Years
Location: Bangalore / Hyderabad
Notice Period: Immediate to 15 Days
Interview Process: 1st Round – Virtual | 2nd Round – F2F
Mandatory Skills
- Python
- PySpark
- SQL
- Azure Data Engineering
Job Description
We are looking for an experienced Azure Data Engineer with 9+ years of experience and strong hands-on expertise in Python, PySpark, SQL, and Azure Data Engineering.
Key Responsibilities
- Develop and maintain scalable data engineering solutions using Azure.
- Build and optimize data processing pipelines using PySpark and Python.
- Write complex SQL queries for data extraction and transformation.
- Work with Azure data services and cloud-based data platforms.
- Perform data processing, transformation, and integration.
- Troubleshoot data pipeline and production issues.
- Collaborate with technical and business teams to deliver data solutions.
Preferred: Immediate to 15 Days joiners.
Job Summary: GCP Data Engineering Lead
Experience: 9+ Years
Location: Bangalore / Hyderabad
Notice Period: Immediate to 15 Days
Key Skills:
- Strong experience in GCP Data Engineering
- Proven Technical Lead / Lead experience
- Strong programming skills in Python
- Hands-on experience with PySpark
- Strong expertise in SQL / PL-SQL
- Good understanding of GCP data services and data engineering concepts
- Experience in designing and developing scalable data pipelines
- Strong problem-solving and technical leadership skills
Roles & Responsibilities:
- Lead the design and development of scalable GCP data engineering solutions.
- Develop and optimize data pipelines using Python, PySpark and SQL/PL-SQL.
- Design data processing solutions and ensure performance and scalability.
- Provide technical leadership, conduct code reviews, and mentor team members.
- Collaborate with business and technical teams to understand requirements and deliver data solutions.
- Troubleshoot issues and ensure quality across the data engineering lifecycle.
Senior Data Engineer – PySpark & Oracle
Experience: 7+ Years
Location: Bangalore
Notice Period: Immediate to 10 Days
Key Skills:
- Strong expertise in Data Modeling, Data Design & Modernization
- Primary skills: PySpark, Oracle SQL/PLSQL
- Secondary skills: Python, ETL & Data Pipelines
- Experience with Kafka and Hadoop
- Exposure to AWS / Azure / GCP
- Good knowledge of Git and JIRA
Roles & Responsibilities:
- Design, develop, and modernize scalable data models and data architecture.
- Develop and optimize data processing solutions using PySpark and Oracle SQL/PLSQL.
- Build and maintain robust ETL workflows and data pipelines.
- Work with Kafka, Hadoop, and cloud platforms for data processing and integration.
- Perform data transformation, optimization, and performance tuning.
- Collaborate with technical teams on data design, development, testing, and deployment.
1st virtual , 2nd round F2F
Python pyspark, SQL, data engineer
5+yrs
Bang/hyderabad
immediate to 15days.










