{"id":122265,"url":"https://alion.io/job/tata-power-data-engineer","title":"Data Engineer","company":{"id":46843,"name":"Tata Power","domain":"tatapower.com","url":"https://alion.io/company/tata-power","size_band":null,"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":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bhubaneswar, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":12500,"max_usd":28000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":12},"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","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":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Hadoop","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"GitHub","optional":true}],"status":"live","first_seen_at":"2026-08-25T08:32:40Z","employer_posted_date":null,"last_verified_at":"2026-08-25T08:32:40Z","board_verified":false,"closed_at":null,"days_open":51,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":51},"description":"We are looking for an enthusiastic and highly motivated Data Engineer with 1 to 3 years of experience and a strong foundation in data engineering, ETL/ELT pipelines, data warehousing, and cloud data platforms. The ideal candidate should have hands-on experience in building data pipelines, working with structured and unstructured data, and supporting analytics and reporting platforms using tools such as Azure Data Factory, Databricks, AWS, and Azure. This role is ideal for candidates who are passionate about building scalable data pipelines, working with large datasets, and developing modern data platforms in cloud environments.\nRequirements:\nExperience: 1-8 years.\nEducation: Bachelor's/Master's in Computer Science or Information Technology.\nBachelor's or master's degree in computer science or information technology.\n1 to 3 years of experience in data engineering, ETL development, data pipelines, or data platform projects.\nHands-on experience in building and maintaining ETL/ELT pipelines.\nExperience working with cloud platforms such as AWS or Azure is preferred.\nTechnical Expertise:\nStrong knowledge of SQL and database concepts.\nProficiency in Python or PySpark for data processing.\nExperience in building ETL/ELT pipelines and data workflows.\nUnderstanding of data warehousing concepts and data modelling (Star Schema, Snowflake Schema).\nExperience working with structured and semi-structured data (CSV, JSON, Parquet, etc. ).\nUnderstanding of data lakes, data warehouses, and lakehouse architecture.\nKnowledge of data quality, data validation, and data reconciliation techniques.\nUnderstanding of batch and streaming data processing concepts.\nExperience performing Exploratory Data Analysis (EDA) and data profiling.\nFrameworks and Tools:\nExperience with Azure Data Factory or AWS Glue for data integration and pipeline development.\nHands-on experience with Databricks and PySpark for data transformation and processing.\nExperience with Azure Data Lake / AWS S3 storage.\nFamiliarity with data warehouses such as Azure Synapse, Redshift, Snowflake, or Databricks SQL.\nUnderstanding of workflow orchestration tools and scheduling pipelines.\nExperience in data ingestion from APIs, databases, flat files, and streaming sources.\nBasic understanding of CI/CD for data pipelines is an added advantage.\nAdditional Knowledge:\nUnderstanding of big data technologies such as Hadoop and Spark.\nFamiliarity with streaming technologies such as Kafka or Azure Event Hub is a plus.\nKnowledge of data governance, metadata management, and data security concepts is an advantage.\nExposure to performance tuning and optimization of data pipelines is preferred.\nPreferred Qualification:\nParticipation in data engineering projects, hackathons, or technical competitions.\nContributions to GitHub projects, technical blogs, or technical documentation are an added advantage.\nRelevant certifications in Azure, AWS, Databricks, or data engineering are a plus.","description_format":"text","description_chars":2953,"description_truncated":false,"requirements":{"experience_years_min":1,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Energy & Utilities","Energy Storage","Solar 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