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Lead I - Data Engineering

Lead I - Data Engineering

UST

5 - 7 years

5 Lacs

kochi thiruvananthapuram

Posted:8 hours ago| Platform:

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Skills Required

lookup python development columns pyspark data warehousing microsoft azure data pipeline azure data factory transformation sql aggregate analytics sql database development etl tool design optimization techniques join design principles logic bigquery etl aws programming

Work Mode

Work from Office

Job Type

Full Time

Job Description

Role Proficiency:

This role requires proficiency in data pipeline development including coding and testing data pipelines for ingesting wrangling transforming and joining data from various sources. Must be skilled in ETL tools such as Informatica Glue Databricks and DataProc with coding expertise in Python PySpark and SQL. Works independently and has a deep understanding of data warehousing solutions including Snowflake BigQuery Lakehouse and Delta Lake. Capable of calculating costs and understanding performance issues related to data solutions.

Outcomes:

Act creatively to develop pipelines and applications by selecting appropriate technical options optimizing application development maintenance and performance using design patterns and reusing proven solutions.rnInterpret requirements to create optimal architecture and design developing solutions in accordance with specifications.
Document and communicate milestones/stages for end-to-end delivery.
Code adhering to best coding standards debug and test solutions to deliver best-in-class quality.
Perform performance tuning of code and align it with the appropriate infrastructure to optimize efficiency.
Validate results with user representatives integrating the overall solution seamlessly.
Develop and manage data storage solutions including relational databases NoSQL databases and data lakes.
Stay updated on the latest trends and best practices in data engineering cloud technologies and big data tools.
Influence and improve customer satisfaction through effective data solutions.

Measures of Outcomes:

Adherence to engineering processes and standards
Adherence to schedule / timelines
Adhere to SLAs where applicable
# of defects post delivery
# of non-compliance issues
Reduction of reoccurrence of known defects
Quickly turnaround production bugs
Completion of applicable technical/domain certifications
Completion of all mandatory training requirements
Efficiency improvements in data pipelines (e.g. reduced resource consumption faster run times).
Average time to detect respond to and resolve pipeline failures or data issues.
Number of data security incidents or compliance breaches.

Outputs Expected:

Code Development:

Develop data processing code independently
ensuring it meets performance and scalability requirements.
Define coding standards
templatesand checklists.
Review code for team members and peers.

Documentation:

Create and review templates
checklistsguidelinesand standards for designprocessesand development.
Create and review deliverable documents
including design documentsarchitecture documentsinfrastructure costingbusiness requirementssource-target mappingstest casesand results.

Configuration:

Define and govern the configuration management plan.
Ensure compliance within the team.

Testing:

Review and create unit test cases
scenariosand execution plans.
Review the test plan and test strategy developed by the testing team.
Provide clarifications and support to the testing team as needed.

Domain Relevance:

Advise data engineers on the design and development of features and components
demonstrating a deeper understanding of business needs.
Learn about customer domains to identify opportunities for value addition.
Complete relevant domain certifications to enhance expertise.

Project Management:

Manage the delivery of modules effectively.

Defect Management:

Perform root cause analysis (RCA) and mitigation of defects.
Identify defect trends and take proactive measures to improve quality.

Estimation:

Create and provide input for effort and size estimation for projects.

Knowledge Management:

Consume and contribute to project-related documents
SharePointlibrariesand client universities.
Review reusable documents created by the team.

Release Management:

Execute and monitor the release process to ensure smooth transitions.

Design Contribution:

Contribute to the creation of high-level design (HLD)
low-level design (LLD)and system architecture for applicationsbusiness componentsand data models.

Customer Interface:

Clarify requirements and provide guidance to the development team.
Present design options to customers and conduct product demonstrations.

Team Management:

Set FAST goals and provide constructive feedback.
Understand team members' aspirations and provide guidance and opportunities for growth.
Ensure team engagement in projects and initiatives.

Certifications:

Obtain relevant domain and technology certifications to stay competitive and informed.

Skill Examples:

Proficiency in SQL Python or other programming languages used for data manipulation.
Experience with ETL tools such as Apache Airflow Talend Informatica AWS Glue Dataproc and Azure ADF.
Hands-on experience with cloud platforms like AWS Azure or Google Cloud particularly with data-related services (e.g. AWS Glue BigQuery).
Conduct tests on data pipelines and evaluate results against data quality and performance specifications.
Experience in performance tuning of data processes.
Expertise in designing and optimizing data warehouses for cost efficiency.
Ability to apply and optimize data models for efficient storage retrieval and processing of large datasets.
Capacity to clearly explain and communicate design and development aspects to customers.
Ability to estimate time and resource requirements for developing and debugging features or components.

Knowledge Examples:

Knowledge Examples

Knowledge of various ETL services offered by cloud providers including Apache PySpark AWS Glue GCP DataProc/DataFlow Azure ADF and ADLF.
Proficiency in SQL for analytics including windowing functions.
Understanding of data schemas and models relevant to various business contexts.
Familiarity with domain-related data and its implications.
Expertise in data warehousing optimization techniques.
Knowledge of data security concepts and best practices.
Familiarity with design patterns and frameworks in data engineering.

Additional Comments:

1. Core Azure Data Engineering Strong experience with Azure Data Factory (ADF) - pipeline design, activities, linked services, triggers, and parameterization. Expertise in ADF Data Flows - transformations, derived columns, joins, aggregations, lookups, and conditional logic. Experience building metadata-driven and config-based pipelines . Hands-on with Azure Data Lake Storage (ADLS) and Azure Blob Storage - managing containers, access, and file movement. Understanding of Azure SQL Database / Azure Synapse Analytics - data loading, schema design, stored procedure integration. Working knowledge of Delta Lake and Parquet file structures. 2. ETL / ELT & Data Processing Solid background in ETL design principles - incremental loads, full loads, upserts, and change data capture (CDC). Good SQL proficiency for complex joins, aggregations, and transformations. Experience in building data pipelines that integrate with Databricks notebooks or Azure Functions. Familiarity with data validation, auditing, and error-handling patterns. 3. Programming & Automation Python - for data transformation, automation scripts, or calling ADF REST APIs. Optional: PySpark - for large-scale processing inside Databricks. JSON and ARM templates - for pipeline deployment and configuration management. 4. Cloud & DevOps Integration Familiarity with Azure DevOps - CI/CD pipelines for ADF (via ARM templates or YAML). Git integration for version control and branch management (main/dev/prod). Basic understanding of monitoring & ing using Azure Monitor / Log Analytics. 5. Data Modeling & Performance Understanding of data warehouse concepts (star schema, fact/dimension design). Query optimization and partitioning strategies for large datasets. Exposure to Synapse, Databricks SQL, or Power BI integration is a plus. The knowledge, experience and qualifications you need - Strong SQL database development skills, including ADF, SSIS - Performance tuning processes dealing with very large data sets - Good all round technical knowledge to help find the best solution to a requirement - Able to deliver well-structured and tested software - Working with senior stakeholders and users - Logical, structured thought processes and good problem-solving - Relationship management and business engagement - Create solutions which can be handed over to first level support in the first instance Hands-on experience in retail, CPG, or supply chain data platforms. Exposure to metadata-driven frameworks (like SCUM, AUDI, or ADF Bridge). Experience working with GCP to Azure migration or BigQuery Azure SQL workloads. Familiarity with data quality frameworks and exception handling mechanisms.

Required Skills

Sql,Azure Data Factory,Azure Dataflow

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Lead I - Data Engineering