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 escalate 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, project leadership, client delivery, technical training, and demonstrated delivery outcomes.

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.

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Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

Job Summary

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 escalate 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, project leadership, client delivery, technical training, and demonstrated delivery outcomes.

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.