{"id":3233519,"url":"https://alion.io/job/engineo-data-engineer","title":"Data Engineer","company":{"id":20100,"name":"Engineo","domain":"engineo.solutions","url":"https://alion.io/company/engineo","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon Neptune","optional":true},{"name":"Apache Hudi","optional":true},{"name":"Apache Iceberg","optional":true},{"name":"Delta Lake","optional":true},{"name":"Flink","optional":true},{"name":"Git","optional":true},{"name":"Great Expectations","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Neo4j","optional":true}],"status":"live","first_seen_at":"2026-10-25T09:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-25T09:35:00Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"The candidate will have responsibilities across the following functions: \n\nETL/ELT Pipeline Architecture and Development: \nArchitect, design, and develop robust, scalable ETL/ELT pipelines for ingesting, transforming, and loading high-volume data from diverse sources, including relational databases, REST APIs, streaming platforms, flat files, and cloud storage.\nLead the end-to-end design of data flows from raw ingestion layers through to curated, consumption-ready data products across data warehouse, data lake, and lakehouse architectures.\nBuild and maintain pipelines using industry-standard frameworks, including Apache Spark, Apache Airflow, dbt, AWS Glue, Azure Data Factory, or Google Dataflow.\nImplement incremental, change data capture (CDC), and real-time streaming ingestion patterns using tools such as Apache Kafka or AWS Kinesis, where applicable.\nDefine and enforce pipeline reliability standards, SLA monitoring, alerting, retry logic, idempotency, and graceful failure handling across all data engineering workloads.\nOwn pipeline performance tuning, query optimisation, partitioning strategies, caching, and resource management to ensure efficient processing of large-scale datasets.\n\nData Modelling and Warehouse Design: \nLead the design and implementation of dimensional and relational data models, including star schema, snowflake schema, and medallion (Bronze/Silver/Gold) architecture for data warehouse and lakehouse environments.\nDefine physical data models, partitioning schemes, and storage strategies optimised for query performance and cost efficiency across cloud platforms.\nCollaborate with data architects to evolve the enterprise data model, ensuring alignment with business domain definitions and downstream consumption requirements.\nDocument and maintain entity-relationship diagrams (ERDs), data dictionaries, and schema version history for all data models under ownership.\nDrive adoption of data modelling best practices and standards across the engineering team.\n\nData Quality, Governance and Lineage: \nDesign and implement comprehensive data quality frameworks, including validation rules, reconciliation checks, anomaly detection, and automated alerting\nto ensure data reliability at every layer.\nEstablish and maintain end-to-end data lineage tracking to provide full visibility of data origin, transformation, and movement across systems.\nLead metadata management and data cataloguing initiatives, ensuring all datasets are well-documented, discoverable, and governed in line with enterprise data standards.\nDefine and enforce data retention, archiving, and access control policies in collaboration with data governance and security teams.\nChampion DataOps practices CI/CD for data pipelines, automated testing, version control, and environment management to improve pipeline reliability and deployment velocity.\n\nCloud Infrastructure and Platform Engineering: \nDesign and manage scalable data infrastructure on cloud platforms AWS (S3 Redshift, Glue, EMR, Kinesis), GCP (BigQuery, Dataflow, Pub/Sub), or Azure (Synapse, Data Factory, ADLS) based on project requirements.\nWork with DevOps and cloud infrastructure teams on infrastructure-as-code (Terraform, CloudFormation) for provisioning and managing data platform components.\nOptimise cloud data platform costs through right-sizing, storage tiering, query optimisation, and workload management strategies.\nEvaluate and introduce new tools, frameworks, and cloud-native services to improve pipeline efficiency, scalability, and maintainability.\nEnsure all data infrastructure meets security, compliance, and data residency requirements applicable to the organisation and its clients.\n\nTechnical Leadership and Mentoring: \nServe as a technical lead for the data engineering team, providing architectural guidance, conducting design reviews, and setting coding and documentation standards.\nMentor and coach junior and mid-level data engineers through hands-on guidance, code reviews, and structured feedback to accelerate their technical growth.\nLead technical discussions, architecture design sessions, and proof-of-concept evaluations for new data engineering initiatives.\nContribute to engineering roadmap planning, sprint estimation, and technical backlog prioritisation in collaboration with the engineering manager.\nEngiNeo Solutions: Drive adoption of best practices in pipeline testing, version control, peer review, and automated quality checks across the team.\n\nCollaboration and Stakeholder Engagement: \nPartner closely with data architects, data scientists, ML engineers, BI developers, and business analysts to deliver high-quality, consumption-ready data products.\nEngage directly with business stakeholders to understand data requirements, translate them into technical specifications, and manage expectations on delivery timelines.\nCollaborate with data governance, security, and compliance teams to ensure all data engineering deliverables meet organisational policy requirements.\nParticipate in Agile ceremonies, sprint planning, standups, retrospectives, and reviews and contribute to continuous improvement of the team's delivery processes.\n\nThe core requirements for the job include the following:\n\nEducation and Technical Skills: \nBachelor's or Master's degree in Computer Science, Information Technology, Electronics, or a related engineering discipline.\n7-10 years of hands-on experience in data engineering, with a strong focus on ETL/ELT pipeline development and data warehouse or lakehouse architecture.\nExpert-level proficiency in Python and SQL, including advanced query writing, window functions, query optimisation, and stored procedures.\nDeep, production-grade experience with at least two of: Apache Spark, Apache Airflow, dbt, AWS Glue, Azure Data Factory, Google Dataflow, or equivalent pipeline frameworks.\nProven experience designing and implementing dimensional data models and warehouse architectures for large-scale analytical workloads.\nHands-on experience with at least one major cloud data platform: AWS (Redshift, S3 Glue, EMR), GCP (BigQuery, Dataflow), or Azure (Synapse Analytics, ADLS, ADF).\nStrong understanding of data quality, data lineage, metadata management, and data governance principles.\n\nGood to Have: \nExperience with modern table formats and lakehouse technologies, such as Delta Lake, Apache Iceberg, or Apache Hudi.\nFamiliarity with real-time and streaming data ingestion using Apache Kafka, Apache Flink, AWS Kinesis, or Google Pub/Sub.\nExposure to infrastructure-as-code tools such as Terraform or CloudFormation for data platform provisioning.\nKnowledge of data ontology concepts, knowledge graphs, or graph databases (Neo4j, Amazon Neptune).\nExperience with DataOps practices, CI/CD pipelines for data, automated pipeline testing frameworks, and Git-based workflow management.\nFamiliarity with data observability tools such as Monte Carlo, Great Expectations, or Soda.\nCloud certifications in AWS, GCP, or Azure data or analytics specialisations.\n\nSoft Skills and Leadership Qualities: \nStrong ownership mindset takes end-to-end accountability for data engineering deliverables and pipeline reliability.\nAbility to communicate complex technical concepts clearly to both technical peers and non-technical business stakeholders.\nCollaborative and team-oriented, works effectively across data, analytics, ML, and business teams in a fast-paced environment.\nStructured problem-solver who can diagnose and resolve complex data pipeline issues under time pressure.\nProactive learner who stays current with evolving data engineering tools, frameworks, and best practices.\nExperience working in Agile delivery environments with a continuous improvement mindset.\nSkills\nApache Spark, ELT, ETL, Python, SQL, Spark, 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