Data Engineer/Analyst – Python, Spark, SQL, Cloud & Data Architecture

Synechron Technologies Pvt. Ltd._INDIA Company; Gurugram District; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Synechron Technologies Pvt. Ltd. in India seeks a Lead – Data Engineer/Data Analyst to lead data and analytics projects, delivering data-driven solutions across data engineering, analytics, governance, and architecture with client and internal teams.

You will design data models, pipelines, data warehouses/lakes, and BI visualizations using Tableau/Power BI, mentor juniors, manage delivery plans, and ensure quality, cost optimization, and timely delivery.

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, or related field.
• 7+ years of experience in Data and Analytics projects with leadership.
• Strong experience with Python, Spark, SQL, data warehousing, data lakes, and BI tools.

Responsibilities

• Lead data analysis, modeling, architecture, integration, and solution-design activities with clients.
• Manage project delivery through planning, scheduling, risk management, and budgets.
• Mentor team members and provide technical guidance across data engineering and analytics.

Skills

Python

SQL

Apache Spark

Data modeling

Data integration

Tableau

Power BI

Education

Bachelor's degree in Computer Science / related field

Tools

Oracle

MySQL

SQL Server

Tableau

Power BI

Job description

Job Summary

Synechron is seeking a Lead – Data Engineer/Data Analyst to lead Data and Analytics projects and deliver data-driven solutions. The role will combine technical expertise in data engineering, analytics, data architecture, modeling, integration, visualization, and governance with project delivery and team leadership responsibilities. The successful candidate will work closely with clients and internal teams to understand business requirements, design effective data solutions, manage delivery plans, and ensure projects are completed on time, within budget, and to agreed quality standards. The role will contribute to business value through reliable data, actionable insights, scalable architectures, and improved decision-making.

Software Requirements

Required SQL: Strong experience with data analysis, data transformation, querying, validation, and database development. Python: Strong experience in data engineering, analytics, automation, or data processing. Apache Spark: Strong experience with distributed data processing and large-scale data workloads. Database technologies: Experience with Oracle, MySQL, and SQL Server. Data warehousing: Strong understanding of data warehouse architecture, modeling, and implementation. Data lakes: Experience with data lake concepts, data ingestion, storage, processing, and governance. Big data platforms: Strong technical understanding of platforms supporting large-scale data processing and analytics. Data modeling: Experience designing conceptual, logical, and physical data models. Data integration: Experience designing and implementing data integration solutions. Data governance: Experience with data quality, standards, ownership, metadata, access, and lifecycle controls. Data visualization and BI: Knowledge of Tableau and Power BI. Cloud data solutions: Familiarity with AWS and Azure data environments. Project delivery tools: Ability to use tools for project planning, delivery schedules, documentation, collaboration, and reporting. Preferred Experience with cloud-native data architectures and data engineering services. Experience delivering data-driven solutions in complex enterprise environments. Experience with data quality automation, metadata management, lineage, and reconciliation. Experience with advanced analytics, reporting, or self-service BI solutions. Certification in data engineering, analytics, cloud, database, or BI technologies. Experience improving data-processing efficiency and optimizing cloud resource usage.

Overall Responsibilities

Lead and manage Data and Analytics projects from requirements definition through delivery and transition. Work closely with clients and internal stakeholders to understand business requirements and develop data-driven solutions. Provide technical expertise and guidance to junior team members and project teams. Oversee data collection, cleansing, transformation, validation, and preparation processes. Design and implement data architectures, data models, data warehouses, data lakes, and data integration solutions. Develop and maintain project plans, delivery schedules, milestones, dependencies, risks, and resource plans. Lead data analysis and modeling activities to address business and analytical requirements. Guide the selection and application of data technologies, tools, platforms, and delivery approaches. Ensure data solutions meet requirements for quality, scalability, maintainability, security, and performance. Lead technical discussions with clients, architects, analysts, engineers, and other stakeholders. Review technical outputs, data models, integration designs, analytical solutions, and project deliverables. Monitor project progress, quality, budget, risks, and delivery timelines. Mentor team members and support knowledge sharing, technical development, and consistent engineering practices. Ensure project deliverables are completed on time, within budget, and in accordance with agreed standards. Promote efficient use of data storage, processing, and cloud resources to support sustainable technology practices.

Technical Skills (By Category)

Programming Languages Essential Python. SQL. Apache Spark development and data-processing capabilities. Ability to write maintainable code for data transformation, validation, automation, and analytics. Experience applying programming and query techniques to large-scale data workloads. Preferred Experience with additional scripting or programming languages used for data engineering, automation, or analytics. Experience developing reusable data-processing frameworks, utilities, or automation components. Databases/Data Management Essential Oracle. MySQL. SQL Server. Data warehousing. Data lakes. Big data platforms. Data modeling and data integration. Data collection, cleansing, preparation, transformation, and validation. Data governance and data quality management. Understanding of data structures, relationships, lineage, ownership, access, and lifecycle management. Preferred Experience with data migration, reconciliation, metadata management, lineage, and master data practices. Experience with database and data-platform performance optimization. Experience designing data solutions for high-volume or complex analytical workloads. Cloud Technologies Essential Familiarity with cloud-based data solutions. AWS. Azure. Understanding of cloud data storage, processing, integration, scalability, availability, and governance considerations. Preferred Experience designing or implementing cloud-native data architectures. Experience with cloud data warehouses, data lakes, processing platforms, and integration services. Experience with cloud monitoring, cost management, resource optimization, and sustainable data-processing practices. Experience supporting cloud migration or data-platform modernization.

Frameworks and Libraries

Essential Apache Spark. Frameworks and libraries used for Python-based data engineering and analytics. Components supporting data ingestion, transformation, validation, integration, and analytics. Tools supporting data visualization and business intelligence, including Tableau and Power BI. Preferred Libraries supporting advanced analytics, data quality, profiling, lineage, and workflow automation. Reusable data-engineering components and shared analytical frameworks. Tools supporting self-service reporting and interactive data exploration.

Development Tools and Methodologies

Essential Data and Analytics project delivery. Project planning and delivery scheduling. Requirements analysis and solution design. Data architecture and modeling. Data integration and pipeline development. Data governance and quality practices. Client and stakeholder collaboration. Technical reviews and delivery reporting. Team leadership, mentoring, and project coordination. Ability to manage multiple tasks, priorities, dependencies, risks, and deliverables. Preferred Agile delivery methodologies. Project portfolio management and governance practices. Automated data-quality checks, testing, monitoring, and deployment. Experience with source control, CI/CD, workflow orchestration, and data pipeline monitoring. Experience establishing data engineering standards and delivery metrics.

Security Protocols

Essential Apply data security and governance practices throughout data collection, storage, processing, integration, and reporting. Protect sensitive business and client data through appropriate access controls, permissions, and secure handling practices. Consider data privacy, retention, classification, lineage, and auditability requirements in solution design. Identify and elevate security, privacy, data-quality, and operational risks. Preferred Experience implementing security controls in cloud-based data platforms. Experience supporting regulatory, audit, privacy, or compliance requirements. Experience with data masking, encryption, role-based access, and secure data integration. Experience incorporating security and governance checks into data delivery processes.

Experience Requirements

Required: Minimum of 7 years of experience in Data and Analytics projects. Experience leading project teams and managing project delivery. Proven track record of delivering data-driven solutions to clients. Strong experience with Python, Apache Spark, SQL, data warehousing, data lakes, big data platforms, data modeling, and data integration. Experience with Oracle, MySQL, SQL Server, Tableau, Power BI, AWS, and Azure is required or preferred according to the specific project scope. Experience managing project plans, delivery schedules, technical discussions, quality, budgets, risks, and stakeholder expectations. Experience mentoring junior team members and providing technical guidance. Industry-specific experience is not mandatory; experience delivering data solutions in complex enterprise environments is preferred. Candidates may also qualify through an equivalent combination of relevant data engineering, data analytics, _

Day-to-Day Activities

Lead data analysis, modeling, architecture, integration, and solution-design activities while working with clients to understand data needs. Manage project delivery through planning, scheduling, progress tracking, risk management, stakeholder coordination, and budget monitoring. Lead technical discussions with clients and internal teams and provide guidance on data platforms, models, governance, quality, and integration approaches. Mentor team members, review deliverables, ensure quality and timely delivery, and make technical decisions within agreed project standards.

Qualifications

A bachelor’s or master’s degree in Computer Science, Information Systems, Engineering, or a related field is required; equivalent relevant education and experience may be considered. Minimum of 7 years of experience in Data and Analytics projects, including project leadership and delivery management. Demonstrated experience with Python, Apache Spark, SQL, databases, data modeling, data integration, data warehousing, data lakes, and big data platforms. Certifications in data engineering, analytics, cloud platforms, databases, BI, or related technologies are preferred but not mandatory. Maintain continuous professional development in data engineering, analytics, cloud platforms, data governance, visualization, project delivery, and emerging data technologies.

Professional Competencies

Apply structured analytical thinking and problem-solving to assess data requirements, resolve data issues, and design practical solutions. Lead project teams, mentor junior team members, and provide technical guidance across data engineering and analytics activities. Communicate data concepts, technical decisions, risks, progress, and recommendations clearly to clients and internal stakeholders. Adapt to evolving business requirements, data technologies, delivery priorities, project constraints, and client needs. Identify opportunities to improve data quality, automation, architecture, analytics, delivery efficiency, and sustainable resource utilization. Manage multiple projects, priorities, dependencies, delivery schedules, budgets, stakeholder expectations, and team workloads effectively.

SYNECHRON’S DIVERSITY & INCLUSION STATEMENT

Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more. All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.

Candidate Application Notice

At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 14,500+, and has 58 offices in 21 countries within key global markets. For more information on the company, please visit our website or LinkedIn community. At Synechron, we are committed to integrating sustainability into our business strategy, ensuring responsible growth while minimizing environmental impact. Employees play a key role in driving our sustainability initiatives, from reducing our carbon footprint to fostering ethical and sustainable business practices across global operations. All positions are required to adhere to our Sustainability and Health Safety standards, demonstrating a commitment to environmental stewardship, workplace safety, and sustainable practices.