Job
Description
Job Summary: Synechron is seeking an experienced Senior Data Engineer with expertise in AWS, Apache Airflow, and DBT to design and implement scalable, reliable data pipelines. The role involves collaborating with data teams and business stakeholders to develop data solutions that enable actionable insights and support organizational decision-making. The ideal candidate will bring data engineering experience, demonstrating strong technical skills, strategic thinking, and the ability to work in a fast-paced, evolving environment. Software Requirements: Required: Strong proficiency in AWS services including S3, Redshift, Lambda, and Glue, with proven hands-on experience Expertise in Apache Airflow for workflow orchestration and pipeline management Extensive experience with DBT for data transformation and modeling Solid knowledge of SQL for data querying and manipulation Preferred: Familiarity with Hadoop, Spark, or other big data technologies Experience with NoSQL databases (e.g., DynamoDB, Cassandra) Knowledge of data governance and security best practices within cloud environments Overall Responsibilities: Lead the design, development, and maintenance of scalable and efficient data pipelines and workflows utilizing AWS, Airflow, and DBT Collaborate with data scientists, analysts, and business teams to gather requirements and translate them into technical solutions Optimize Extract, Transform, Load (ETL) processes to enhance data quality, integrity, and timeliness Monitor pipeline performance, troubleshoot issues, and implement improvements to ensure operational excellence Enforce data management, governance, and security protocols across all data flows Mentor junior data engineers and promote best practices within the team Stay current with emerging data technologies and industry trends, recommending innovations for the data ecosystem Technical Skills (By Category): Programming Languages: Essential: SQL, Python (preferred for scripting and automation) Preferred: Spark, Scala, Java (for big data integration) Databases/Data Management: Extensive experience with data warehousing (Redshift, Snowflake, or similar) and relational databases (MySQL, PostgreSQL) Familiarity with NoSQL databases such as DynamoDB or Cassandra is a plus Cloud Technologies: AWS cloud platform, leveraging services like S3, Lambda, Glue, Redshift, and IAM security features Frameworks and Libraries: Apache Airflow, dbt, and related data orchestration and transformation tools Development Tools and Methodologies: Git, Jenkins, CI/CD pipelines, Agile/Scrum environment experience Security Protocols: Knowledge of data encryption, access control, and compliance standards in cloud data engineering Experience Requirements: At least 8 years of professional experience in data engineering or related roles with a focus on cloud ecosystems and big data pipelines Demonstrated experience designing and managing end-to-end data workflows in AWS environments Proven success in collaborating with cross-functional teams and translating business requirements into technical solutions Prior experience mentoring junior engineers and leading data projects is highly desirable Day-to-Day Activities: Develop, deploy, and monitor scalable data pipelines using AWS, Airflow, and DBT Collaborate regularly with data scientists, analysts, and business stakeholders to refine data requirements and deliver impactful solutions Troubleshoot production data pipeline issues to resolve data quality or performance bottlenecks Conduct code reviews, optimize existing workflows, and implement automation to improve efficiency Document data architecture, pipelines, and governance practices for knowledge sharing and compliance Keep abreast of emerging data tools and industry best practices, proposing enhancements to existing systems Qualifications: Bachelor’s degree in Computer Science, Data Science, Engineering, or related field; Master’s degree preferred Professional certifications such as AWS Certified Data Analytics – Specialty or related credentials are advantageous Commitment to continuous professional development and staying current with industry trends Professional Competencies: Strong analytical, problem-solving, and critical thinking skills Excellent communication abilities to effectively liaise with technical and business teams Proven leadership in mentoring team members and managing project deliverables Ability to work independently, prioritize tasks, and adapt to changing business needs Innovative mindset focused on scalable, efficient, and sustainable data solutions
